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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
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
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
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.
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.
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
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
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
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
Claude is AI for all of us. Whether you're brainstorming alone or building with a team of thousands, Claude is here to help.
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
🚀 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
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
The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.
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
A family of powerful, small language models (SLMs) with groundbreaking performance at low cost and low latency
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
Social post update on the release and availability of o3 and o4-mini via ChatGPT and API.
Earlier Claude 3.5 version with improved understanding and reasoning over previous models.
Claude 3.7 release focusing on safer and more reliable AI assistant capabilities.
Overview of Claude 3 series and their use in various AI assistant applications.
Latest Claude model focusing on robust, ethical, and high-performance AI assistant features.
Integration of Cohere’s Command R+ model with Azure for enhanced enterprise AI solutions.
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
DeepSeek’s AI coding assistant fine-tuned for instructive programming help.
Earlier news API update with improvements in summarization and text annotation from multi-source content.
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.
Latest DeepSeek API update focused on more accurate, efficient news summarization.
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
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
Lightweight, faster variant of Gemini 1.5 optimized for lower latency.
Smaller 8 billion parameter Gemini 1.5 Flash model balancing performance and efficiency.
Advanced Gemini 1.5 Pro model for multi-turn conversations and complex reasoning.
Interface for testing Gemini 2.0 Flash, a fast, cost-efficient language model variant from Google.
Streamlined Gemini 2.0 Flash model for rapid inference and multitasking.
Preview of Google’s Gemini 2.0 "Flash" variant with focus on deep reasoning and cost-effective performance .
Experimental Gemini 2.0 Pro model in AI Studio, optimized for high-end multimodal reasoning tasks.
Lightweight, fast variant of Gemini 2.5, ideal for real-time applications with reduced cost and strong performance.
Earlier experimental release of Gemini 2.5 Pro, optimized for multimodal inputs and large-context understanding.
Advanced Gemini model with deep reasoning and multimodal capabilities, available via Google AI Studio preview.
Another experimental prompt/model config in Gemini 2.x line focused on system-level integration.
Early experimental release of Gemini 2.x series for development and tuning.
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
API documentation for language model usage on OpenBigModel platform.
Chinese AI open platform providing access to large-scale models and APIs.
Improved version with 1M-token context window, better instruction-following, and lighter variants (mini/nano).
Enhanced generalist model with strong emotional intelligence, reduced hallucinations, and broad multilingual abilities.
Introduction to GPT-4o, a variant designed for advanced, efficient multimodal AI.
Compact, cost-efficient version of GPT-4o tailored for resource-conscious applications.
OpenAI’s faster and cheaper GPT-4 Turbo alongside GPT-4 with strong multimodal and reasoning skills.
xAI’s flagship model with 10× compute, advanced reasoning modes, DeepSearch integration, and multimodal support.
Vision model API doc covering object detection, classification, and related image-processing tasks.
Official Meta page describing Llama 3 model series and capabilities.
Meta’s detailed update on Llama 3.1 model family improvements and applications.
Meta’s Llama 4 Maverick 17B model fine-tuned for instruction tasks with long context support.
Llama 4 Scout variant optimized for faster inference and multitasking.
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 .
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
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.
Introduces o1 reasoning model in API with function calling, vision support, structured outputs, Pref‑Fine‑Tuning, and real-time/WebRTC updates .
Guides explaining how to adjust reasoning effort and optimize o1’s prompt/control usage .
Official documentation for o1, detailing its reasoning-effort control, multimodal input, cost, and usage tiers.
Introduction of OpenAI’s o3 and o4-mini models, balancing powerful reasoning with tool use and multimodal image support.
Combines deep-step reasoning (o3) with lightweight, cost-effective reasoning variant (o4‑mini), each with multimodal tool use support.
LLM focused on creativity and idea generation for writers
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Medical domain LLM designed for healthcare content and communication.
Slightly smaller variant optimized for creative content generation.
Writer.com’s Palmyra X5 LLM tailored for advanced writing and content generation tasks.
Medium-sized Phi-3 model with 4k context window and instruction tuning.
Microsoft Azure’s Phi 3 model redefining large-scale language model capabilities in the cloud.
Smaller Phi-3 model variant with extended 8k token context and instruction capabilities.
Mistral’s Pixtral model optimized for instruction tuning with large parameter size.
Visual-language Qwen2.5 model combining vision and text, optimized for instructive use cases, hosted on Hugging Face.
Qwen 2.5 Visual-Language 32B model fine-tuned for instruction following tasks.
Larger Qwen 2.5 Visual-Language 72B model optimized for instruction-based multimodal tasks.
Blog about Qwen 2 Visual-Language models focused on integrating vision and text understanding.
Blog detailing Qwen 2.5 Max, a large-scale multimodal model with enhanced vision and language capabilities.
Aliyun’s guide on their vision AI studio tools for building and deploying vision-language models.
Overview of Qwen 3, a state-of-the-art large language model supporting many languages and large context windows.
Red Hat® Enterprise Linux® AI is a foundation model platform to seamlessly develop, test, and run Granite family large language models (LLMs) for enterprise applications.
Solar Pro is a cutting-edge large language model (LLM) developed by Upstage, designed to deliver high-performance natural language processing capabilities while operating efficiently on a single GPU.
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.
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.
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:
LLMs are equipped with features such as text generation, summarization, and sentiment analysis to complete a wide range of NLP tasks.
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.
Language models can basically be classified into two main categories — statistical models and language models designed on deep neural networks.
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 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.
LLMs come with a suite of benefits that can transform countless aspects of how businesses and individuals work. Listed below are some common advantages.
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.
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.
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:
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:
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.
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:
There are several other alternatives to explore in place of a large language model software that can be tailored to specific departmental workflows.
The large language model space is constantly evolving, and what's current now could change in the near future as new research and developments occur. Here are some trends that are currently ruling the LLM domain.
Researched and written by Matthew Miller
Reviewed and edited by Sinchana Mistry