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Best Large Language Models (LLMs) Software

Matthew Miller
MM
Researched and written by Matthew Miller

Large language models (LLM) are advanced artificial intelligence (AI) systems specifically engineered to comprehend, interpret, and generate human-like text from a wide array of inputs. Leveraging state-of-the-art machine learning (ML) techniques, massive training datasets, and profound architectures, these models can accomplish a broad spectrum of natural language tasks, making them indispensable tools for businesses across all sectors. The tasks range from translation, summarization, question answering, and conversation to more nuanced applications such as sentiment analysis, text classification, and creative content generation.

The LLMs in this category are being employed to revolutionize customer service through intelligent chatbots, augment content creation with auto-writing capabilities, streamline market research with sentiment analysis, and much more. With multilingual proficiency, many can be adaptable to global markets, breaking down language barriers and facilitating cross-cultural communication.

The advancements in LLM technology also signal the era of automation in many language-related tasks, thereby reducing manual labor and improving efficiency. They bring transformative change to the user experience, adding a layer of personalization and interactivity that was previously unattainable.

This category differs from the AI chatbots software category, which focuses on standalone platforms that allow users to interact and engage with large language models, and the synthetic media software category, which consists of tools for business users to create AI-generated media. These LLM solutions, instead, are designed to be more versatile and foundational and can be integrated into a wide range of applications, not just limited to chatbots or synthetic media.

To qualify for inclusion in the Large Language Models (LLM) category, a product must:

Offer a large-scale language model capable of comprehending and generating human-like text from a variety of inputs, made available for commercial use
Provide robust and secure APIs or integration tools, enabling businesses from various sectors to seamlessly incorporate the model into their existing systems or processes
Have comprehensive mechanisms in place to tackle potential issues related to data privacy, ethical use, and content moderation, ensuring user trust and regulatory compliance
Deliver reliable customer support and extensive documentation, along with consistent updates and improvements, thereby aiding users in the effective integration and usage of the model while also ensuring its ongoing relevance and adaptability to changing requirements

Best Large Language Models (LLMs) Software At A Glance

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Highest Performer:
Easiest to Use:
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G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.

109 Listings in Large Language Models (LLMs) Available
(194)4.4 out of 5
View top Consulting Services for Gemini
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  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    DeepMind's Gemini is a suite of advanced AI models and products, designed to push the boundaries of artificial intelligence. It represents DeepMind's next-generation system, building on the foundation

    Users
    • Research Analyst
    • Software Engineer
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 41% Small-Business
    • 38% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Gemini Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Useful
    114
    Ease of Use
    90
    Helpful
    54
    Features
    37
    Performance Improvement
    36
    Cons
    AI Limitations
    52
    Inaccuracy
    33
    Usage Limitations
    27
    Context Understanding
    26
    Inaccurate Responses
    25
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini features and usability ratings that predict user satisfaction
    8.5
    Quality of Support
    Average: 8.0
    8.3
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    7.6
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Company Website
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

DeepMind's Gemini is a suite of advanced AI models and products, designed to push the boundaries of artificial intelligence. It represents DeepMind's next-generation system, building on the foundation

Users
  • Research Analyst
  • Software Engineer
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 41% Small-Business
  • 38% Mid-Market
Gemini Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Useful
114
Ease of Use
90
Helpful
54
Features
37
Performance Improvement
36
Cons
AI Limitations
52
Inaccuracy
33
Usage Limitations
27
Context Understanding
26
Inaccurate Responses
25
Gemini features and usability ratings that predict user satisfaction
8.5
Quality of Support
Average: 8.0
8.3
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
7.6
Bias Mitigation
Average: 8.0
Seller Details
Seller
Google
Company Website
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
(147)4.3 out of 5
1st Easiest To Use in Large Language Models (LLMs) software
View top Consulting Services for Meta Llama 3
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  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Experience the state-of-the-art performance of Llama 3, an openly accessible model that excels at language nuances, contextual understanding, and complex tasks like translation and dialogue generation

    Users
    • Software Engineer
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 57% Small-Business
    • 24% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Meta Llama 3 Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Accuracy
    35
    Ease of Use
    31
    Speed
    30
    Open-Source
    26
    Helpful
    24
    Cons
    Limitations
    26
    Slow Performance
    18
    Poor Response Quality
    16
    Inaccuracy
    13
    Limited Understanding
    11
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Meta Llama 3 features and usability ratings that predict user satisfaction
    7.1
    Quality of Support
    Average: 8.0
    6.7
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    6.3
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2008
    HQ Location
    Menlo Park, CA
    Twitter
    @Meta
    13,563,890 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    126,735 employees on LinkedIn®
    Ownership
    NASDAQ: META
Product Description
How are these determined?Information
This description is provided by the seller.

Experience the state-of-the-art performance of Llama 3, an openly accessible model that excels at language nuances, contextual understanding, and complex tasks like translation and dialogue generation

Users
  • Software Engineer
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 57% Small-Business
  • 24% Mid-Market
Meta Llama 3 Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Accuracy
35
Ease of Use
31
Speed
30
Open-Source
26
Helpful
24
Cons
Limitations
26
Slow Performance
18
Poor Response Quality
16
Inaccuracy
13
Limited Understanding
11
Meta Llama 3 features and usability ratings that predict user satisfaction
7.1
Quality of Support
Average: 8.0
6.7
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
6.3
Bias Mitigation
Average: 8.0
Seller Details
Year Founded
2008
HQ Location
Menlo Park, CA
Twitter
@Meta
13,563,890 Twitter followers
LinkedIn® Page
www.linkedin.com
126,735 employees on LinkedIn®
Ownership
NASDAQ: META

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  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    BERT, short for Bidirectional Encoder Representations from Transformers, is a machine learning (ML) framework for natural language processing. In 2018, Google developed this algorithm to improve conte

    Users
    • Data Scientist
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 54% Small-Business
    • 30% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • BERT Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    9
    Accuracy
    5
    Natural Language Processing
    4
    Open Source
    4
    Performance Improvement
    4
    Cons
    High Computational Demand
    6
    Improvement Needed
    5
    High Resource Consumption
    3
    Context Understanding
    2
    Expensive
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • BERT features and usability ratings that predict user satisfaction
    8.4
    Quality of Support
    Average: 8.0
    8.1
    Content Moderation
    Average: 8.1
    8.1
    Contextual Understanding
    Average: 8.3
    7.9
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

BERT, short for Bidirectional Encoder Representations from Transformers, is a machine learning (ML) framework for natural language processing. In 2018, Google developed this algorithm to improve conte

Users
  • Data Scientist
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 54% Small-Business
  • 30% Mid-Market
BERT Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
9
Accuracy
5
Natural Language Processing
4
Open Source
4
Performance Improvement
4
Cons
High Computational Demand
6
Improvement Needed
5
High Resource Consumption
3
Context Understanding
2
Expensive
2
BERT features and usability ratings that predict user satisfaction
8.4
Quality of Support
Average: 8.0
8.1
Content Moderation
Average: 8.1
8.1
Contextual Understanding
Average: 8.3
7.9
Bias Mitigation
Average: 8.0
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    GPT-3 powers the next generation of apps Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.

    Users
    No information available
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 57% Small-Business
    • 30% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • GPT3 Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    3
    Accuracy
    2
    Performance Improvement
    2
    Content Creation
    1
    Creativity Enhancement
    1
    Cons
    Expensive
    1
    Hallucinations
    1
    Information Overload
    1
    Limited Accessibility
    1
    Limited Customization
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT3 features and usability ratings that predict user satisfaction
    8.6
    Quality of Support
    Average: 8.0
    8.4
    Content Moderation
    Average: 8.1
    8.7
    Contextual Understanding
    Average: 8.3
    7.8
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

GPT-3 powers the next generation of apps Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.

Users
No information available
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 57% Small-Business
  • 30% Mid-Market
GPT3 Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
3
Accuracy
2
Performance Improvement
2
Content Creation
1
Creativity Enhancement
1
Cons
Expensive
1
Hallucinations
1
Information Overload
1
Limited Accessibility
1
Limited Customization
1
GPT3 features and usability ratings that predict user satisfaction
8.6
Quality of Support
Average: 8.0
8.4
Content Moderation
Average: 8.1
8.7
Contextual Understanding
Average: 8.3
7.8
Bias Mitigation
Average: 8.0
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
(43)4.6 out of 5
View top Consulting Services for GPT4
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    GPT-4o is our most advanced multimodal model that’s faster and cheaper than GPT-4 Turbo with stronger vision capabilities. The model has 128K context and an October 2023 knowledge cutoff.

    Users
    No information available
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 58% Small-Business
    • 26% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • GPT4 Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Chat Communication
    3
    Ease of Use
    3
    Knowledge Access
    3
    Accuracy
    2
    Helpful
    2
    Cons
    Expensive
    3
    Inaccurate Responses
    1
    Lack of Creativity
    1
    Limitations
    1
    Limited Customization
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT4 features and usability ratings that predict user satisfaction
    8.3
    Quality of Support
    Average: 8.0
    8.1
    Content Moderation
    Average: 8.1
    8.8
    Contextual Understanding
    Average: 8.3
    8.1
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

GPT-4o is our most advanced multimodal model that’s faster and cheaper than GPT-4 Turbo with stronger vision capabilities. The model has 128K context and an October 2023 knowledge cutoff.

Users
No information available
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 58% Small-Business
  • 26% Mid-Market
GPT4 Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Chat Communication
3
Ease of Use
3
Knowledge Access
3
Accuracy
2
Helpful
2
Cons
Expensive
3
Inaccurate Responses
1
Lack of Creativity
1
Limitations
1
Limited Customization
1
GPT4 features and usability ratings that predict user satisfaction
8.3
Quality of Support
Average: 8.0
8.1
Content Moderation
Average: 8.1
8.8
Contextual Understanding
Average: 8.3
8.1
Bias Mitigation
Average: 8.0
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    First introduced in 2019, Megatron sparked a wave of innovation in the AI community, enabling researchers and developers to utilize the underpinnings of this library to further LLM advancements. Today

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 71% Small-Business
    • 17% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Megatron-LM Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Efficiency
    1
    Natural Language Processing
    1
    Performance Improvement
    1
    Cons
    High Resource Consumption
    1
    Limited Knowledge
    1
    Outdated Information
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Megatron-LM features and usability ratings that predict user satisfaction
    8.5
    Quality of Support
    Average: 8.0
    8.8
    Content Moderation
    Average: 8.1
    8.8
    Contextual Understanding
    Average: 8.3
    8.6
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    NVIDIA
    Year Founded
    1993
    HQ Location
    Santa Clara, CA
    Twitter
    @nvidia
    2,363,899 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    39,703 employees on LinkedIn®
    Ownership
    NVDA
Product Description
How are these determined?Information
This description is provided by the seller.

First introduced in 2019, Megatron sparked a wave of innovation in the AI community, enabling researchers and developers to utilize the underpinnings of this library to further LLM advancements. Today

Users
No information available
Industries
No information available
Market Segment
  • 71% Small-Business
  • 17% Mid-Market
Megatron-LM Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Efficiency
1
Natural Language Processing
1
Performance Improvement
1
Cons
High Resource Consumption
1
Limited Knowledge
1
Outdated Information
1
Megatron-LM features and usability ratings that predict user satisfaction
8.5
Quality of Support
Average: 8.0
8.8
Content Moderation
Average: 8.1
8.8
Contextual Understanding
Average: 8.3
8.6
Bias Mitigation
Average: 8.0
Seller Details
Seller
NVIDIA
Year Founded
1993
HQ Location
Santa Clara, CA
Twitter
@nvidia
2,363,899 Twitter followers
LinkedIn® Page
www.linkedin.com
39,703 employees on LinkedIn®
Ownership
NVDA
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any w

    Users
    No information available
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 42% Small-Business
    • 32% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • GPT2 Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Text Generation
    8
    Content Creation
    7
    Ease of Use
    5
    Knowledge Access
    5
    Versatility
    4
    Cons
    Data Inaccuracy
    5
    Inaccurate Responses
    5
    Context Understanding
    3
    Outdated Information
    3
    Poor Understanding
    3
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT2 features and usability ratings that predict user satisfaction
    8.5
    Quality of Support
    Average: 8.0
    8.5
    Content Moderation
    Average: 8.1
    8.5
    Contextual Understanding
    Average: 8.3
    8.0
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any w

Users
No information available
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 42% Small-Business
  • 32% Enterprise
GPT2 Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Text Generation
8
Content Creation
7
Ease of Use
5
Knowledge Access
5
Versatility
4
Cons
Data Inaccuracy
5
Inaccurate Responses
5
Context Understanding
3
Outdated Information
3
Poor Understanding
3
GPT2 features and usability ratings that predict user satisfaction
8.5
Quality of Support
Average: 8.0
8.5
Content Moderation
Average: 8.1
8.5
Contextual Understanding
Average: 8.3
8.0
Bias Mitigation
Average: 8.0
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The ef

    Users
    No information available
    Industries
    • Information Technology and Services
    Market Segment
    • 70% Small-Business
    • 20% Mid-Market
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • T5 features and usability ratings that predict user satisfaction
    8.5
    Quality of Support
    Average: 8.0
    8.2
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    8.0
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The ef

Users
No information available
Industries
  • Information Technology and Services
Market Segment
  • 70% Small-Business
  • 20% Mid-Market
T5 features and usability ratings that predict user satisfaction
8.5
Quality of Support
Average: 8.0
8.2
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
8.0
Bias Mitigation
Average: 8.0
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    StableLM 3B 4E1T is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs. The model architecture is transformer-based with partial R

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 36% Enterprise
    • 36% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • StableLM Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Efficiency
    5
    Ease of Use
    4
    Performance Improvement
    4
    Accuracy
    2
    Helpful
    2
    Cons
    Technical Issues
    4
    Data Security
    3
    High Resource Consumption
    2
    Low Accuracy
    2
    Slow Performance
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • StableLM features and usability ratings that predict user satisfaction
    9.3
    Quality of Support
    Average: 8.0
    8.6
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    8.9
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    London
    Twitter
    @StabilityAI
    236,948 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    176 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

StableLM 3B 4E1T is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs. The model architecture is transformer-based with partial R

Users
No information available
Industries
No information available
Market Segment
  • 36% Enterprise
  • 36% Mid-Market
StableLM Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Efficiency
5
Ease of Use
4
Performance Improvement
4
Accuracy
2
Helpful
2
Cons
Technical Issues
4
Data Security
3
High Resource Consumption
2
Low Accuracy
2
Slow Performance
2
StableLM features and usability ratings that predict user satisfaction
9.3
Quality of Support
Average: 8.0
8.6
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
8.9
Bias Mitigation
Average: 8.0
Seller Details
HQ Location
London
Twitter
@StabilityAI
236,948 Twitter followers
LinkedIn® Page
www.linkedin.com
176 employees on LinkedIn®
(56)4.4 out of 5
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Claude is AI for all of us. Whether you're brainstorming alone or building with a team of thousands, Claude is here to help.

    Users
    No information available
    Industries
    • Marketing and Advertising
    • Computer Software
    Market Segment
    • 71% Small-Business
    • 20% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Claude Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Useful
    24
    Ease of Use
    17
    Helpful
    17
    Accuracy
    9
    Communication
    9
    Cons
    Usage Limitations
    19
    AI Limitations
    10
    Inaccurate Recognition
    8
    Accuracy Issues
    5
    Context Understanding
    5
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Claude features and usability ratings that predict user satisfaction
    7.6
    Quality of Support
    Average: 8.0
    6.7
    Content Moderation
    Average: 8.1
    7.7
    Contextual Understanding
    Average: 8.3
    6.7
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Anthropic
    HQ Location
    San Francisco, California
    Twitter
    @AnthropicAI
    574,848 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,574 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Claude is AI for all of us. Whether you're brainstorming alone or building with a team of thousands, Claude is here to help.

Users
No information available
Industries
  • Marketing and Advertising
  • Computer Software
Market Segment
  • 71% Small-Business
  • 20% Mid-Market
Claude Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Useful
24
Ease of Use
17
Helpful
17
Accuracy
9
Communication
9
Cons
Usage Limitations
19
AI Limitations
10
Inaccurate Recognition
8
Accuracy Issues
5
Context Understanding
5
Claude features and usability ratings that predict user satisfaction
7.6
Quality of Support
Average: 8.0
6.7
Content Moderation
Average: 8.1
7.7
Contextual Understanding
Average: 8.3
6.7
Bias Mitigation
Average: 8.0
Seller Details
Seller
Anthropic
HQ Location
San Francisco, California
Twitter
@AnthropicAI
574,848 Twitter followers
LinkedIn® Page
www.linkedin.com
1,574 employees on LinkedIn®
(8)4.2 out of 5
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 50% Small-Business
    • 38% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Mistral 7B Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Efficiency
    3
    Performance Improvement
    2
    Speed
    2
    Time-saving
    2
    Accuracy
    1
    Cons
    Inaccurate Responses
    2
    Poor Understanding
    2
    Complexity
    1
    Lack of Creativity
    1
    Limited Functionality
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Mistral 7B features and usability ratings that predict user satisfaction
    8.6
    Quality of Support
    Average: 8.0
    7.8
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    8.9
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases. Mistral 7B is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. It’s released

Users
No information available
Industries
No information available
Market Segment
  • 50% Small-Business
  • 38% Mid-Market
Mistral 7B Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Efficiency
3
Performance Improvement
2
Speed
2
Time-saving
2
Accuracy
1
Cons
Inaccurate Responses
2
Poor Understanding
2
Complexity
1
Lack of Creativity
1
Limited Functionality
1
Mistral 7B features and usability ratings that predict user satisfaction
8.6
Quality of Support
Average: 8.0
7.8
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
8.9
Bias Mitigation
Average: 8.0
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    🚀 Falcon-40B Falcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 l

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 75% Small-Business
    • 25% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Falcon-40B Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    1
    Features Variety
    1
    Cons
    Integration Issues
    1
    Poor Customer Support
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Falcon-40B features and usability ratings that predict user satisfaction
    7.1
    Quality of Support
    Average: 8.0
    9.6
    Content Moderation
    Average: 8.1
    9.2
    Contextual Understanding
    Average: 8.3
    7.5
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1,104 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

🚀 Falcon-40B Falcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 l

Users
No information available
Industries
No information available
Market Segment
  • 75% Small-Business
  • 25% Enterprise
Falcon-40B Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
1
Features Variety
1
Cons
Integration Issues
1
Poor Customer Support
1
Falcon-40B features and usability ratings that predict user satisfaction
7.1
Quality of Support
Average: 8.0
9.6
Content Moderation
Average: 8.1
9.2
Contextual Understanding
Average: 8.3
7.5
Bias Mitigation
Average: 8.0
Seller Details
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1,104 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettle

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 67% Mid-Market
    • 33% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • RoBERTa Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Accuracy
    1
    Customization
    1
    Ease of Use
    1
    Efficiency
    1
    Knowledge Access
    1
    Cons
    Complex Setup
    1
    Slow Performance
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • RoBERTa features and usability ratings that predict user satisfaction
    8.9
    Quality of Support
    Average: 8.0
    7.8
    Content Moderation
    Average: 8.1
    8.9
    Contextual Understanding
    Average: 8.3
    9.4
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettle

Users
No information available
Industries
No information available
Market Segment
  • 67% Mid-Market
  • 33% Enterprise
RoBERTa Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Accuracy
1
Customization
1
Ease of Use
1
Efficiency
1
Knowledge Access
1
Cons
Complex Setup
1
Slow Performance
1
RoBERTa features and usability ratings that predict user satisfaction
8.9
Quality of Support
Average: 8.0
7.8
Content Moderation
Average: 8.1
8.9
Contextual Understanding
Average: 8.3
9.4
Bias Mitigation
Average: 8.0
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 100% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • BLOOMChat features and usability ratings that predict user satisfaction
    10.0
    Quality of Support
    Average: 8.0
    8.3
    Content Moderation
    Average: 8.1
    10.0
    Contextual Understanding
    Average: 8.3
    10.0
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2017
    HQ Location
    Palo Alto
    Twitter
    @SambaNovaAI
    45,999 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    442 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

Users
No information available
Industries
No information available
Market Segment
  • 100% Small-Business
BLOOMChat features and usability ratings that predict user satisfaction
10.0
Quality of Support
Average: 8.0
8.3
Content Moderation
Average: 8.1
10.0
Contextual Understanding
Average: 8.3
10.0
Bias Mitigation
Average: 8.0
Seller Details
Year Founded
2017
HQ Location
Palo Alto
Twitter
@SambaNovaAI
45,999 Twitter followers
LinkedIn® Page
www.linkedin.com
442 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    GlobalChat is a unified AI workspace built for creators, developers, researchers, and business teams who are tired of juggling multiple tools and subscriptions. By bringing together industry-leading m

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 100% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • globalChat features and usability ratings that predict user satisfaction
    6.7
    Quality of Support
    Average: 8.0
    8.3
    Content Moderation
    Average: 8.1
    8.3
    Contextual Understanding
    Average: 8.3
    8.3
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

GlobalChat is a unified AI workspace built for creators, developers, researchers, and business teams who are tired of juggling multiple tools and subscriptions. By bringing together industry-leading m

Users
No information available
Industries
No information available
Market Segment
  • 100% Small-Business
globalChat features and usability ratings that predict user satisfaction
6.7
Quality of Support
Average: 8.0
8.3
Content Moderation
Average: 8.1
8.3
Contextual Understanding
Average: 8.3
8.3
Bias Mitigation
Average: 8.0
Seller Details
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Users
    No information available
    Industries
    No information available
    Market Segment
    • 100% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • IBM Granite Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Free Services
    1
    Open Source
    1
    Search Features
    1
    UI Design
    1
    Updates
    1
    Cons
    This product has not yet received any negative sentiments.
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • IBM Granite features and usability ratings that predict user satisfaction
    3.3
    Quality of Support
    Average: 8.0
    6.7
    Content Moderation
    Average: 8.1
    5.0
    Contextual Understanding
    Average: 8.3
    6.7
    Bias Mitigation
    Average: 8.0
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    IBM
    Year Founded
    1911
    HQ Location
    Armonk, NY
    Twitter
    @IBM
    709,764 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    331,391 employees on LinkedIn®
    Ownership
    SWX:IBM
Users
No information available
Industries
No information available
Market Segment
  • 100% Small-Business
IBM Granite Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Free Services
1
Open Source
1
Search Features
1
UI Design
1
Updates
1
Cons
This product has not yet received any negative sentiments.
IBM Granite features and usability ratings that predict user satisfaction
3.3
Quality of Support
Average: 8.0
6.7
Content Moderation
Average: 8.1
5.0
Contextual Understanding
Average: 8.3
6.7
Bias Mitigation
Average: 8.0
Seller Details
Seller
IBM
Year Founded
1911
HQ Location
Armonk, NY
Twitter
@IBM
709,764 Twitter followers
LinkedIn® Page
www.linkedin.com
331,391 employees on LinkedIn®
Ownership
SWX:IBM
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    A family of powerful, small language models (SLMs) with groundbreaking performance at low cost and low latency

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 100% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Phi-3 open models Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Easy Integrations
    1
    Efficiency
    1
    Cons
    Limitations
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Phi-3 open models features and usability ratings that predict user satisfaction
    8.3
    Quality of Support
    Average: 8.0
    0.0
    No information available
    8.3
    Contextual Understanding
    Average: 8.3
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,002,464 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    237,523 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

A family of powerful, small language models (SLMs) with groundbreaking performance at low cost and low latency

Users
No information available
Industries
No information available
Market Segment
  • 100% Enterprise
Phi-3 open models Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Easy Integrations
1
Efficiency
1
Cons
Limitations
1
Phi-3 open models features and usability ratings that predict user satisfaction
8.3
Quality of Support
Average: 8.0
0.0
No information available
8.3
Contextual Understanding
Average: 8.3
0.0
No information available
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,002,464 Twitter followers
LinkedIn® Page
www.linkedin.com
237,523 employees on LinkedIn®
Ownership
MSFT
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Athene 70B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    NexusFlow
    HQ Location
    Palo Alto, California
    LinkedIn® Page
    www.linkedin.com
    18 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Athene 70B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
NexusFlow
HQ Location
Palo Alto, California
LinkedIn® Page
www.linkedin.com
18 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and scalability of training LLMs on the Cerebr

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Cerebras-GPT features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Cerebras
    Year Founded
    2016
    HQ Location
    Los Altos, CA
    Twitter
    @CerebrasSystems
    34,013 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    464 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and scalability of training LLMs on the Cerebr

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Cerebras-GPT features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Cerebras
Year Founded
2016
HQ Location
Los Altos, CA
Twitter
@CerebrasSystems
34,013 Twitter followers
LinkedIn® Page
www.linkedin.com
464 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Social post update on the release and availability of o3 and o4-mini via ChatGPT and API.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • ChatGPT 4o Latest features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Social post update on the release and availability of o3 and o4-mini via ChatGPT and API.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
ChatGPT 4o Latest features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Earlier Claude 3.5 version with improved understanding and reasoning over previous models.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Claude 3.5 Sonnet features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Anthropic
    HQ Location
    San Francisco, California
    Twitter
    @AnthropicAI
    574,848 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,574 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Earlier Claude 3.5 version with improved understanding and reasoning over previous models.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Claude 3.5 Sonnet features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Anthropic
HQ Location
San Francisco, California
Twitter
@AnthropicAI
574,848 Twitter followers
LinkedIn® Page
www.linkedin.com
1,574 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Claude 3.7 release focusing on safer and more reliable AI assistant capabilities.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Claude 3.7 Sonnet features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Anthropic
    HQ Location
    San Francisco, California
    Twitter
    @AnthropicAI
    574,848 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,574 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Claude 3.7 release focusing on safer and more reliable AI assistant capabilities.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Claude 3.7 Sonnet features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Anthropic
HQ Location
San Francisco, California
Twitter
@AnthropicAI
574,848 Twitter followers
LinkedIn® Page
www.linkedin.com
1,574 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Overview of Claude 3 series and their use in various AI assistant applications.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Claude 3 Opus features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Anthropic
    HQ Location
    San Francisco, California
    Twitter
    @AnthropicAI
    574,848 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,574 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Overview of Claude 3 series and their use in various AI assistant applications.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Claude 3 Opus features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Anthropic
HQ Location
San Francisco, California
Twitter
@AnthropicAI
574,848 Twitter followers
LinkedIn® Page
www.linkedin.com
1,574 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Latest Claude model focusing on robust, ethical, and high-performance AI assistant features.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Claude 4 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Anthropic
    HQ Location
    San Francisco, California
    Twitter
    @AnthropicAI
    574,848 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,574 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Latest Claude model focusing on robust, ethical, and high-performance AI assistant features.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Claude 4 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Anthropic
HQ Location
San Francisco, California
Twitter
@AnthropicAI
574,848 Twitter followers
LinkedIn® Page
www.linkedin.com
1,574 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Command A features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Cohere
    Year Founded
    2019
    HQ Location
    Toronto, Ontario
    LinkedIn® Page
    www.linkedin.com
    38 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Command A features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Cohere
Year Founded
2019
HQ Location
Toronto, Ontario
LinkedIn® Page
www.linkedin.com
38 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Integration of Cohere’s Command R+ model with Azure for enhanced enterprise AI solutions.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Command R+ features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Cohere
    Year Founded
    2019
    HQ Location
    Toronto, Ontario
    LinkedIn® Page
    www.linkedin.com
    38 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Integration of Cohere’s Command R+ model with Azure for enhanced enterprise AI solutions.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Command R+ features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Cohere
Year Founded
2019
HQ Location
Toronto, Ontario
LinkedIn® Page
www.linkedin.com
38 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The Quantum Cognitive Content Models (QCCM) are an AI-powered marketing tool developed by TravsX. Designed with deep marketing intelligence, QCCM crafts content that mirrors the strategic thinking of

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • ContentX features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    TravsX
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The Quantum Cognitive Content Models (QCCM) are an AI-powered marketing tool developed by TravsX. Designed with deep marketing intelligence, QCCM crafts content that mirrors the strategic thinking of

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
ContentX features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
TravsX
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    DeepSeek’s AI coding assistant fine-tuned for instructive programming help.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DeepSeek Coder V2 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    DeepSeek
    Year Founded
    2023
    HQ Location
    Hangzhou
    LinkedIn® Page
    www.linkedin.com
    124 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

DeepSeek’s AI coding assistant fine-tuned for instructive programming help.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
DeepSeek Coder V2 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
DeepSeek
Year Founded
2023
HQ Location
Hangzhou
LinkedIn® Page
www.linkedin.com
124 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Earlier news API update with improvements in summarization and text annotation from multi-source content.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DeepSeek R1 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    DeepSeek
    Year Founded
    2023
    HQ Location
    Hangzhou
    LinkedIn® Page
    www.linkedin.com
    124 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Earlier news API update with improvements in summarization and text annotation from multi-source content.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
DeepSeek R1 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
DeepSeek
Year Founded
2023
HQ Location
Hangzhou
LinkedIn® Page
www.linkedin.com
124 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    DeepSeek R2 is the next-generation AI model with 1.2T parameters, advanced cost reduction, vision accuracy, and more. Follow us for the latest updates.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DeepSeek R2 LLM features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

DeepSeek R2 is the next-generation AI model with 1.2T parameters, advanced cost reduction, vision accuracy, and more. Follow us for the latest updates.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
DeepSeek R2 LLM features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Latest DeepSeek API update focused on more accurate, efficient news summarization.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DeepSeek V3 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    DeepSeek
    Year Founded
    2023
    HQ Location
    Hangzhou
    LinkedIn® Page
    www.linkedin.com
    124 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Latest DeepSeek API update focused on more accurate, efficient news summarization.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
DeepSeek V3 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
DeepSeek
Year Founded
2023
HQ Location
Hangzhou
LinkedIn® Page
www.linkedin.com
124 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    AI Squared's dlite-v2-1.5b is a large language model which is derived from OpenAI's large GPT-2 model and fine-tuned on a corpus of 15k records (Databricks' "Dolly 15k" Dataset) to help it exhibit cha

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DLite features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

AI Squared's dlite-v2-1.5b is a large language model which is derived from OpenAI's large GPT-2 model and fine-tuned on a corpus of 15k records (Databricks' "Dolly 15k" Dataset) to help it exhibit cha

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
DLite features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • FastChat-T5 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    LMSYS
    Year Founded
    2016
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
FastChat-T5 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
LMSYS
Year Founded
2016
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Lightweight, faster variant of Gemini 1.5 optimized for lower latency.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 1.5 Flash features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Lightweight, faster variant of Gemini 1.5 optimized for lower latency.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 1.5 Flash features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Smaller 8 billion parameter Gemini 1.5 Flash model balancing performance and efficiency.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 1.5 Flash 8B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Smaller 8 billion parameter Gemini 1.5 Flash model balancing performance and efficiency.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 1.5 Flash 8B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Advanced Gemini 1.5 Pro model for multi-turn conversations and complex reasoning.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 1.5 Pro features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Advanced Gemini 1.5 Pro model for multi-turn conversations and complex reasoning.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 1.5 Pro features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Interface for testing Gemini 2.0 Flash, a fast, cost-efficient language model variant from Google.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.0 Flash features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Interface for testing Gemini 2.0 Flash, a fast, cost-efficient language model variant from Google.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.0 Flash features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Streamlined Gemini 2.0 Flash model for rapid inference and multitasking.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.0 Flash Lite features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Streamlined Gemini 2.0 Flash model for rapid inference and multitasking.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.0 Flash Lite features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Preview of Google’s Gemini 2.0 "Flash" variant with focus on deep reasoning and cost-effective performance .

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.0 Flash Thinking features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Preview of Google’s Gemini 2.0 "Flash" variant with focus on deep reasoning and cost-effective performance .

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.0 Flash Thinking features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Experimental Gemini 2.0 Pro model in AI Studio, optimized for high-end multimodal reasoning tasks.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.0 Pro Exp features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Experimental Gemini 2.0 Pro model in AI Studio, optimized for high-end multimodal reasoning tasks.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.0 Pro Exp features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Lightweight, fast variant of Gemini 2.5, ideal for real-time applications with reduced cost and strong performance.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.5 Flash Preview features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Lightweight, fast variant of Gemini 2.5, ideal for real-time applications with reduced cost and strong performance.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.5 Flash Preview features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Earlier experimental release of Gemini 2.5 Pro, optimized for multimodal inputs and large-context understanding.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.5 Pro Exp features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Earlier experimental release of Gemini 2.5 Pro, optimized for multimodal inputs and large-context understanding.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.5 Pro Exp features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Advanced Gemini model with deep reasoning and multimodal capabilities, available via Google AI Studio preview.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini 2.5 Pro Preview features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Advanced Gemini model with deep reasoning and multimodal capabilities, available via Google AI Studio preview.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini 2.5 Pro Preview features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Another experimental prompt/model config in Gemini 2.x line focused on system-level integration.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini Exp 1121 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Another experimental prompt/model config in Gemini 2.x line focused on system-level integration.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini Exp 1121 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Early experimental release of Gemini 2.x series for development and tuning.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemini Exp 1206 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Product Description
How are these determined?Information
This description is provided by the seller.

Early experimental release of Gemini 2.x series for development and tuning.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemini Exp 1206 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
0 ratings
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Experience Google's most capable open model with multimodal capabilities and 128K context window. Try Gemma 3 for free on here. https://gemma3.co with rich examples showcasing various applications and

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemma3 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Gemma3
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Experience Google's most capable open model with multimodal capabilities and 128K context window. Try Gemma 3 for free on here. https://gemma3.co with rich examples showcasing various applications and

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemma3 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Gemma3
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemma 3 12B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemma 3 12B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Gemma 3 27B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,750,646 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    310,061 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Gemma 3 27B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,750,646 Twitter followers
LinkedIn® Page
www.linkedin.com
310,061 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    API documentation for language model usage on OpenBigModel platform.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GLM 4 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Zhipu AI
    HQ Location
    Beijing, CN
    LinkedIn® Page
    www.linkedin.com
    57 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

API documentation for language model usage on OpenBigModel platform.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GLM 4 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Zhipu AI
HQ Location
Beijing, CN
LinkedIn® Page
www.linkedin.com
57 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Chinese AI open platform providing access to large-scale models and APIs.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GLM 4 Plus features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Zhipu AI
    HQ Location
    Beijing, CN
    LinkedIn® Page
    www.linkedin.com
    57 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Chinese AI open platform providing access to large-scale models and APIs.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GLM 4 Plus features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Zhipu AI
HQ Location
Beijing, CN
LinkedIn® Page
www.linkedin.com
57 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Improved version with 1M-token context window, better instruction-following, and lighter variants (mini/nano).

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT 4.1 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Improved version with 1M-token context window, better instruction-following, and lighter variants (mini/nano).

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GPT 4.1 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Enhanced generalist model with strong emotional intelligence, reduced hallucinations, and broad multilingual abilities.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT 4.5 Preview features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Enhanced generalist model with strong emotional intelligence, reduced hallucinations, and broad multilingual abilities.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GPT 4.5 Preview features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
0 ratings
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Introduction to GPT-4o, a variant designed for advanced, efficient multimodal AI.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT 4o features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Introduction to GPT-4o, a variant designed for advanced, efficient multimodal AI.

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Industries
No information available
Market Segment
No information available
GPT 4o features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Compact, cost-efficient version of GPT-4o tailored for resource-conscious applications.

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    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT 4o Mini features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Compact, cost-efficient version of GPT-4o tailored for resource-conscious applications.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GPT 4o Mini features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    OpenAI’s faster and cheaper GPT-4 Turbo alongside GPT-4 with strong multimodal and reasoning skills.

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    No information available
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    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • GPT 4 Turbo features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

OpenAI’s faster and cheaper GPT-4 Turbo alongside GPT-4 with strong multimodal and reasoning skills.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
GPT 4 Turbo features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    xAI’s flagship model with 10× compute, advanced reasoning modes, DeepSearch integration, and multimodal support.

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    Industries
    No information available
    Market Segment
    No information available
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    Expand/Collapse User Satisfaction
  • Grok 3 Preview features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    xAI
    Year Founded
    2022
    HQ Location
    Asnières-sur-Seine, FR
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

xAI’s flagship model with 10× compute, advanced reasoning modes, DeepSearch integration, and multimodal support.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Grok 3 Preview features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
xAI
Year Founded
2022
HQ Location
Asnières-sur-Seine, FR
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Vision model API doc covering object detection, classification, and related image-processing tasks.

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    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Hunyuan Turbos features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Tencent
    Year Founded
    1998
    HQ Location
    Shenzhen, Guangdong
    Twitter
    @TencentGlobal
    51,145 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    93,297 employees on LinkedIn®
    Ownership
    OTC: TCEHY
Product Description
How are these determined?Information
This description is provided by the seller.

Vision model API doc covering object detection, classification, and related image-processing tasks.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Hunyuan Turbos features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Tencent
Year Founded
1998
HQ Location
Shenzhen, Guangdong
Twitter
@TencentGlobal
51,145 Twitter followers
LinkedIn® Page
www.linkedin.com
93,297 employees on LinkedIn®
Ownership
OTC: TCEHY
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Official Meta page describing Llama 3 model series and capabilities.

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    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Llama 3.1 405B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Official Meta page describing Llama 3 model series and capabilities.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 3.1 405B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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    No information available
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    No information available
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    Expand/Collapse User Satisfaction
  • Llama 3.1 70B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 3.1 70B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Meta’s detailed update on Llama 3.1 model family improvements and applications.

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    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Llama 3.1 Nemotron Ultra 253B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Meta’s detailed update on Llama 3.1 model family improvements and applications.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 3.1 Nemotron Ultra 253B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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    No information available
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    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Llama 3.3 Nemotron 49B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 3.3 Nemotron 49B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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    No information available
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    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Llama 3 70B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 3 70B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Meta’s Llama 4 Maverick 17B model fine-tuned for instruction tasks with long context support.

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    No information available
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    No information available
  • User Satisfaction
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  • Llama 4 Maverick 17B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Meta’s Llama 4 Maverick 17B model fine-tuned for instruction tasks with long context support.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 4 Maverick 17B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Llama 4 Scout variant optimized for faster inference and multitasking.

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    No information available
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    No information available
  • User Satisfaction
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  • Llama 4 Scout 17B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Meta
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    1 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Llama 4 Scout variant optimized for faster inference and multitasking.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Llama 4 Scout 17B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Meta
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
1 employees on LinkedIn®
  • Overview
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  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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    No information available
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    No information available
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  • Mistral Large features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
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  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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No information available
Mistral Large features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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    No information available
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    No information available
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  • Mistral Medium features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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No information available
Mistral Medium features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Released May 2025, offers “at or above” 90% of Claude 3.7 performance, priced competitively ($0.40/$2 per token) and available across major cloud platforms .

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    No information available
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    No information available
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  • Mistral Medium 3 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Released May 2025, offers “at or above” 90% of Claude 3.7 performance, priced competitively ($0.40/$2 per token) and available across major cloud platforms .

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Mistral Medium 3 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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    No information available
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  • Mixtral 8x22B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Mixtral 8x22B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by MosaicML. MPT-7B is part of the family of MosaicPretrainedTransformer (MP

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    No information available
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  • MPT-7B features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    MosaicML
    Year Founded
    2021
    LinkedIn® Page
    www.linkedin.com
    72 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by MosaicML. MPT-7B is part of the family of MosaicPretrainedTransformer (MP

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
MPT-7B features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
MosaicML
Year Founded
2021
LinkedIn® Page
www.linkedin.com
72 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Neospace is a B2B Global AI startup utilizing Large Finance Models to assist financial services enterprises in reimagining, enhacing, and implementing credit scoring and allocation dollars saved.

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    No information available
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    No information available
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  • NeoLang features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Neospace
    Year Founded
    2023
    HQ Location
    Uberlândia, BR
    LinkedIn® Page
    www.linkedin.com
    56 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Neospace is a B2B Global AI startup utilizing Large Finance Models to assist financial services enterprises in reimagining, enhacing, and implementing credit scoring and allocation dollars saved.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
NeoLang features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Neospace
Year Founded
2023
HQ Location
Uberlândia, BR
LinkedIn® Page
www.linkedin.com
56 employees on LinkedIn®
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Introduces o1 reasoning model in API with function calling, vision support, structured outputs, Pref‑Fine‑Tuning, and real-time/WebRTC updates .

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o1 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Introduces o1 reasoning model in API with function calling, vision support, structured outputs, Pref‑Fine‑Tuning, and real-time/WebRTC updates .

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o1 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Guides explaining how to adjust reasoning effort and optimize o1’s prompt/control usage .

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o1 Mini features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Guides explaining how to adjust reasoning effort and optimize o1’s prompt/control usage .

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o1 Mini features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Official documentation for o1, detailing its reasoning-effort control, multimodal input, cost, and usage tiers.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o1 Preview features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Official documentation for o1, detailing its reasoning-effort control, multimodal input, cost, and usage tiers.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o1 Preview features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
0 ratings
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Introduction of OpenAI’s o3 and o4-mini models, balancing powerful reasoning with tool use and multimodal image support.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o3 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Introduction of OpenAI’s o3 and o4-mini models, balancing powerful reasoning with tool use and multimodal image support.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o3 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o3 Mini features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o3 Mini features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o3 Mini High features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o3 Mini High features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Combines deep-step reasoning (o3) with lightweight, cost-effective reasoning variant (o4‑mini), each with multimodal tool use support.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • o4 Mini features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    OpenAI
    Year Founded
    2015
    HQ Location
    San Francisco, CA
    Twitter
    @OpenAI
    4,200,524 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,933 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Combines deep-step reasoning (o3) with lightweight, cost-effective reasoning variant (o4‑mini), each with multimodal tool use support.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
o4 Mini features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
OpenAI
Year Founded
2015
HQ Location
San Francisco, CA
Twitter
@OpenAI
4,200,524 Twitter followers
LinkedIn® Page
www.linkedin.com
1,933 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    LLM focused on creativity and idea generation for writers

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Palmyra Creative features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Writer
    HQ Location
    Kolkata, India
Product Description
How are these determined?Information
This description is provided by the seller.

LLM focused on creativity and idea generation for writers

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Palmyra Creative features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Writer
HQ Location
Kolkata, India
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Financial domain-specialized LLM variant for finance-related writing and analysis.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Palmyra Fin features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Writer
    HQ Location
    Kolkata, India
Product Description
How are these determined?Information
This description is provided by the seller.

Financial domain-specialized LLM variant for finance-related writing and analysis.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Palmyra Fin features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Writer
HQ Location
Kolkata, India
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Medical domain LLM designed for healthcare content and communication.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Palmyra Med features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Writer
    HQ Location
    Kolkata, India
Product Description
How are these determined?Information
This description is provided by the seller.

Medical domain LLM designed for healthcare content and communication.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Palmyra Med features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Writer
HQ Location
Kolkata, India
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Slightly smaller variant optimized for creative content generation.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Palmyra X4 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Writer
    HQ Location
    Kolkata, India
Product Description
How are these determined?Information
This description is provided by the seller.

Slightly smaller variant optimized for creative content generation.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Palmyra X4 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Writer
HQ Location
Kolkata, India
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Writer.com’s Palmyra X5 LLM tailored for advanced writing and content generation tasks.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Palmyra X5 features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Writer
    HQ Location
    Kolkata, India
Product Description
How are these determined?Information
This description is provided by the seller.

Writer.com’s Palmyra X5 LLM tailored for advanced writing and content generation tasks.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Palmyra X5 features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Writer
HQ Location
Kolkata, India
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Medium-sized Phi-3 model with 4k context window and instruction tuning.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Phi 3 Medium 4k features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,002,464 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    237,523 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Medium-sized Phi-3 model with 4k context window and instruction tuning.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Phi 3 Medium 4k features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,002,464 Twitter followers
LinkedIn® Page
www.linkedin.com
237,523 employees on LinkedIn®
Ownership
MSFT
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Microsoft Azure’s Phi 3 model redefining large-scale language model capabilities in the cloud.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Phi 3 Mini 128k features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,002,464 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    237,523 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Microsoft Azure’s Phi 3 model redefining large-scale language model capabilities in the cloud.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Phi 3 Mini 128k features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,002,464 Twitter followers
LinkedIn® Page
www.linkedin.com
237,523 employees on LinkedIn®
Ownership
MSFT
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Smaller Phi-3 model variant with extended 8k token context and instruction capabilities.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Phi 3 Small 8k features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,002,464 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    237,523 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Smaller Phi-3 model variant with extended 8k token context and instruction capabilities.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Phi 3 Small 8k features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,002,464 Twitter followers
LinkedIn® Page
www.linkedin.com
237,523 employees on LinkedIn®
Ownership
MSFT
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Mistral’s Pixtral model optimized for instruction tuning with large parameter size.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
    Market Segment
    No information available
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Pixtral Large features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
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  • Seller Details
    Seller
    Mistral
    Year Founded
    2023
    HQ Location
    Paris, France
    Twitter
    @MistralAI
    151,300 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    33 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Mistral’s Pixtral model optimized for instruction tuning with large parameter size.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Pixtral Large features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Seller
Mistral
Year Founded
2023
HQ Location
Paris, France
Twitter
@MistralAI
151,300 Twitter followers
LinkedIn® Page
www.linkedin.com
33 employees on LinkedIn®
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Visual-language Qwen2.5 model combining vision and text, optimized for instructive use cases, hosted on Hugging Face.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
    Industries
    No information available
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    No information available
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    Expand/Collapse User Satisfaction
  • Qwen 2.5 Max features and usability ratings that predict user satisfaction
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
    0.0
    No information available
  • Seller Details
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  • Seller Details
    Twitter
    @alibaba_cloud
    1,059,398 Twitter followers
Product Description
How are these determined?Information
This description is provided by the seller.

Visual-language Qwen2.5 model combining vision and text, optimized for instructive use cases, hosted on Hugging Face.

We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
Industries
No information available
Market Segment
No information available
Qwen 2.5 Max features and usability ratings that predict user satisfaction
0.0
No information available
0.0
No information available
0.0
No information available
0.0
No information available
Seller Details
Twitter
@alibaba_cloud
1,059,398 Twitter followers
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Qwen 2.5 Visual-Language 32B model fine-tuned for instruction following tasks.

    We don't have enough data from reviews to share who uses this product. Write a review to contribute, or learn more about review generation.
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Learn More About Large Language Models (LLMs) Software

Large language models (LLMs) are machine learning models developed to understand and interact with human language at scale. These advanced artificial intelligence (AI) systems are trained on vast amounts of text data to predict plausible language and maintain a natural flow.

What are large language models (LLMs)?

LLMs are a type of Generative AI models that use deep learning and large text-based data sets to perform various natural language processing (NLP) tasks.

These models analyze probability distributions over word sequences, allowing them to predict the most likely next word within a sentence based on context. This capability fuels content creation, document summarization, language translation, and code generation. 

The term "large” refers to the number of parameters in the model, which are essentially the weights it learns during training to predict the next token in a sequence, or it can also refer to the size of the dataset used for training.

How do large language models (LLMs) work?

LLMs are designed to understand the probability of a single token or sequence of tokens in a longer sequence. The model learns these probabilities by repeatedly analyzing examples of text and understanding which words and tokens are more likely to follow others. 

The training process for LLMs is multi-stage and involves unsupervised learning, self-supervised learning, and deep learning. A key component of this process is the self-attention mechanism, which helps LLMs understand the relationship between words and concepts. It assigns a weight or score to each token within the data to establish its relationship with other tokens.

Here’s a brief rundown of the whole process:

  • A large amount of language data is fed to the LLM from various sources such as books, websites, code, and other forms of written text.
  • The model comprehends the building blocks of language and identifies how words are used and sequenced through pattern recognition with unsupervised learning.
  • Self-supervised learning is used to understand context and word relationships by predicting the following words.
  • Deep learning with neural networks learns language's overall meaning and structure, going beyond just predicting the next word.
  • The self-attention mechanism refines the understanding by assigning a score to each token to establish its influence on other tokens. During training, scores (or weights) are learned, indicating the relevance of all tokens in the sequence to the current token being processed and giving more attention to relevant tokens during prediction.

What are the common features of large language models (LLMs)?

LLMs are equipped with features such as text generation, summarization, and sentiment analysis to complete a wide range of NLP tasks.

  • Human-like text generation across various genres and formats, from business reports to technical emails to basic scripts tailored to specific instructions. 
  • Multilingual support for translating comments, documentation, and user interfaces into multiple languages, facilitating global applications and seamless cross-lingual communication.
  • Understanding context for accurately comprehending language nuances and providing appropriate responses during conversations and analyses.
  • Content summarization recapitulates complex technical documents, research papers, or API references for easy understanding of key points.
  • Sentiment analysis categorizes opinions expressed in text as positive, negative, or neutral, making them useful for social media monitoring, customer feedback analysis, and market research.  
  • Conversational AI and chatbots powered by LLM simulate human-like dialogue, understand user intent, answer user questions, or provide basic troubleshooting steps.
  • Code completion analyzes an existing code to report typos and suggests completions. Some advanced LLMs can even generate entire functions based on the context. It increases development speed, boosts productivity, and tackles repetitive coding tasks.
  • Error identification looks for grammatical errors or inconsistencies in writing and bugs or anomalies in code to help maintain high code and writing quality and reduce debugging time.
  • Adaptability allows LLMs to be fine-tuned for specific applications and perform better in legal document analysis or technical support tasks.
  • Scalability processes vast amounts of information quickly and accommodates the needs of both small businesses and large enterprises.

Who uses large language models (LLMs)? 

LLMs are becoming increasingly popular across various industries because they can process and generate text in creative ways. Below are some businesses that interact with LLMs more often.

  • Content creation and media companies produce significant content, such as news articles, blogs, and marketing materials, by utilizing LLMs to automate and enhance their content creation processes.
  • Customer service providers with large customer service operations, including call centers, online support, and chat services, power intelligent chatbots, and virtual assistants using LLMs to improve response times and customer satisfaction.
  • E-commerce and retail platforms use LLMs to generate product descriptions and offer personalized shopping experiences and customer service interactions, enhancing the overall shopping experience.
  • Financial services providers like banks, investment firms, and insurance companies benefit from LLMs by automating report generation, providing customer support, and personalizing financial advice, thus improving efficiency and customer engagement.
  • Education and e-learning platforms offering educational content and tutoring services use LLMs to create personalized learning experiences, automate grading, and provide instant feedback to students.
  • Healthcare providers use LLMs for patient support, medical documentation, and research, LLMs can analyze and interpret medical texts, support diagnosis processes, and offer personalized patient advice.
  • Technology and software development companies can use LLMs to generate documentation, provide coding assistance, and automate customer support, especially for troubleshooting and handling technical queries.

Types of large language models (LLMs)

Language models can basically be classified into two main categories — statistical models and language models designed on deep neural networks.

Statistical language models

These probabilistic models use statistical techniques to predict the likelihood of a word or sequence of words appearing in a given context. They analyze large corpora of text to learn the patterns of language. 

N-gram models and hidden Markov models (HMMs) are two examples. 

N-gram models analyze sequences of words (n-grams) to predict the probability of the next word appearing. The probability of a word's occurrence is estimated based on the occurrence of the words preceding it within a fixed window of size 'n.' 

For example, consider the sentence, "The cat sat on the mat." In a trigram (3-gram) model, the probability of the word "mat" occurring after the sequence "sat on the" is calculated based on the frequency of this sequence in the training data.

Neural language models

Neural language models utilize neural networks to understand language patterns and word relationships to generate text. They surpass traditional statistical models in detecting complex relationships and dependencies within text. 

Transformer models like GPT use self-attention mechanisms to assess the significance of each word in a sentence, predicting the following word based on contextual dependencies. For example, if we consider the phrase "The cat sat on the," the transformer model might predict "mat" as the next word based on the context provided. 

Among large language models, there are also two primary types — open-domain models and domain-specific models.

  • Open-domain models are designed to perform various tasks without needing customization, making them useful for brainstorming, idea generation, and writing assistance. Examples of open-domain models include generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT). 
  • Domain-specific models: Domain-specific models are customized for specific fields, offering precise and accurate outputs. These models are particularly useful in medicine, law, and scientific research, where expertise is crucial. They are trained or fine-tuned on datasets relevant to the domain in question. Examples of domain-specific LLMs include BioBERT (for biomedical texts) and FinBERT (for financial texts).

Benefits of large language models (LLMs)

LLMs come with a suite of benefits that can transform countless aspects of how businesses and individuals work. Listed below are some common advantages.

  • Increased productivity: LLMs simplify workflows and accelerate project completion by automating repetitive tasks.
  • Improved accuracy: Minimizing inaccuracies is crucial in financial analysis, legal document review, and research domains. LLMs enhance work quality by reducing errors in tasks like data entry and analysis.
  • Cost-effectiveness: LLMs reduce resource requirements, leading to substantial cost savings for businesses of all sizes.
  • Accelerated development cycles: The process from code generation and debugging to research and documentation gets faster for software development tasks, leading to quicker product launches.
  • Enhanced customer engagement: LLM-powered chatbots like ChatGPT enable swift responses to customer inquiries, round-the-clock support, and personalized marketing, creating a more immersive brand interaction.
  • Advanced research capabilities: With LLMs capable of summarizing complex data and sourcing relevant information, research processes become simplified.
  • Data-driven insights: Trained to analyze large datasets, LLMs can extract trends and insights that support data-driven decision-making.

Applications of large language models

LLMs are used in various domains to solve complex problems, reduce the amount of manual work, and open up new possibilities for businesses and people.

  • Keyword research: Analyzing vast amounts of search data helps identify trends and recommend keywords to optimize content for search engines.
  • Market research: Processing user feedback, social media conversations, and market reports uncover insights into consumer behavior, sentiment, and emerging market trends.
  • Content creation: Generating written content such as articles, product descriptions, and social media posts, saves time and resources while maintaining a consistent voice.
  • Malware analysis: Identifying potential malware signatures, suggesting preventive measures by analyzing patterns and code, and generating reports help assist cybersecurity professionals.
  • Translation: Enabling more accurate and natural-sounding translations, LLMs provide multilingual context-aware translation services.
  • Code development: Writing and reviewing code, suggesting syntax corrections, auto-completing code blocks, and generating code snippets within a given context.
  • Sentiment analysis: Analyzing text data to understand the emotional tone and sentiment behind words.
  • Customer support: Engaging with users, answering questions, providing recommendations, and automating customer support tasks, enhance the customer experience with quick responses and 24/7 support.

How much does LLM software cost?

The cost of an LLM depends on multiple factors, like type of license, word usage, token usage, and API call consumptions. The top contenders of LLMs are GPT-4, GPT-Turbo, Llama 3.1, Gemini, and Claude, which offer different payment plans like subscription-based billing for small, mid, and enterprise businesses, tiered billing based on features, tokens, and API integrations and pay-per-use based on actual usage and model capacity and enterprise custom pricing for larger organizations. 

Mostly, LLM software is priced according to the number of tokens consumed and words processed by the model. For example, GPT-4 by OpenAI charges $0.03 per 1000 input tokens and $0.06 for output. Llama 3.1 and Gemini are open-source LLMs that charge between $0.05 to $0.10 per 1000 input tokens and an average of 100 API calls. While the pricing portfolio for every LLM software varies depending on your business type, version, and input data quality, it has become evidently more affordable and budget-friendly with no compromise to processing quality.

Limitations of large language model (LLM) software

While LLMs have boundless benefits, inattentive usage can also lead to grave consequences. Below are the limitations of LLMs that teams should steer clear of:

  • Plagiarism: Copying and pasting text from the LLM platform directly on your blog or other marketing media will raise a case of plagiarism. As the data processed by the LLM is mostly internet-scraped, the chances of content duplication and replication become significantly higher. 
  • Content bias: LLM platforms can alter or change the cause of events, narratives, incidents, statistics, and numbers, as well as inflate data that can be highly misleading and dangerous. Because of limited training abilities, these platforms have a strong chance of generating factually incorrect content that offends people.
  • Hallucination: LLMs even hallucinate and don't correctly register the user's input prompt. Though they might have gotten similar prompts before and know how to answer, they reply in a hallucinated state and don't give you access to data. Writing a follow-up prompt can get LLMs out of this stage and functional again. 
  • Cybersecurity and data privacy: LLMs transfer critical, company-sensitive data to public cloud storage systems that make your data more prone to data breaches, vulnerabilities, and zero-day attacks. 
  • Skills gap: Deploying and maintaining LLMs requires specialized knowledge, and there may be a skills gap in current teams that needs to be addressed through hiring or training.

How to choose the best large language model (LLM) for your business?

Selecting the right LLM software can impact the success of your projects. To choose the model that suits your needs best, consider the following criteria:

  • Use case: Each model has strengths, whether generating content, providing coding assistance, creating chatbots for customer support, or analyzing data. Determine the primary task the LLM will perform and look for models that excel in that specific use case.
  • Model size and capacity: Consider the model's size, which often correlates with capacity and processing needs. Larger models can perform various tasks but require more computational resources. Smaller models may be more cost-effective and sufficient for less complex tasks.
  • Accuracy: Evaluate the LLM's accuracy by reviewing benchmarks or conducting tests. Accuracy is critical — an error-prone model could negatively impact user experience and work efficiency.
  • Performance: Assess the model's speed and responsiveness, especially if real-time processing is required.
  • Training data and pre-training: Determine the breadth and diversity of the training data. Models pre-trained on extensive, varied datasets tend to work better across inputs. However, models trained on niche datasets may perform better for specialized applications.
  • Customization: If your application has unique needs, consider whether the LLM allows for customization or fine-tuning with your data to better tailor its outputs.
  • Cost: Factor in the total cost of ownership, including initial licensing fees, computational costs for training and inference, and any ongoing fees for updates or maintenance.
  • Data security: Look for models that offer security features and compliance with data protection laws relevant to your region or industry.
  • Availability and licensing: Some models are open-source, while others may require a commercial license. Licensing terms can dictate the scope of use, such as whether it's available for commercial applications or has any usage limits.

It's worthwhile to test multiple models in a controlled environment to directly compare how they meet your specific criteria before making a final decision.

LLM implementation

The implementation of an LLM is a continuous process. Regular assessments, upgrades, and re-training are necessary to ensure the technology meets its intended objectives. Here's how to approach the implementation process:

  • Define objectives and scope: Clearly define your project goals and success metrics from the outset to specify what you wish to achieve using an LLM. Identify areas where automation or cognitive enhancements can add value.
  • Data privacy and compliance: Choose an LLM with solid security measures that comply with data protection regulations relevant to your industry, such as GDPR. Establish data handling procedures that preserve user privacy.
  • Model selection: Evaluate whether a general-purpose model like GPT-3 better suits your needs or if a domain-specific model would provide more precise functionality. 
  • Integration and infrastructure: Determine whether you will use the LLM as a cloud service or host it on-premises, considering the computational and memory requirements, potential scalability needs, and latency sensitivities. Account for the API endpoints, SDKs, or libraries you'll need.
  • Training and fine-tuning: Allocate resources for training and validation and tune the model through continuous learning from new data.
  • Content moderation and quality control: Implement systems to oversee the LLM-generated content to ensure that the outputs align with your organizational standards and suit your audience.
  • Continuous evaluation and improvement: Build an evaluation framework to regularly assess your LLM's performance against your objectives. Capture user feedback, monitor performance metrics, and be ready to re-train or update your model to adapt to evolving data patterns or business needs.

Alternatives to LLM software

There are several other alternatives to explore in place of a large language model software that can be tailored to specific departmental workflows. 

  • Natural language understanding (NLU) tools facilitate computer comprehension of human language. NLU enables machines to understand, interpret, and derive meaning from human language. It involves text understanding, semantic analysis, entity recognition, sentiment analysis, and more. NLU is crucial for various applications, such as virtual assistants, chatbots, sentiment analysis tools, and information retrieval systems.
  • Natural language generation (NLG) tools convert structured information into coherent human language text. It is used in language translation, summarization, report generation, conversational agents, and content creation.