Claude Opus 4 is Anthropic's most powerful model and the best coding model in the world, leading on SWE-bench (72.5%) and Terminal-bench (43.2%). It delivers sustained performance on long-running tasks that require focused effort and thousands of steps, with the ability to work continuously for several hours. Features include extended thinking with tool use, parallel tool execution, improved memory capabilities, and significantly reduced shortcut behaviors.

Claude Opus 4

Claude Sonnet 4 significantly improves on Sonnet 3.7's capabilities, excelling in coding with a state-of-the-art 72.7% on SWE-bench. The model balances performance and efficiency, with enhanced steerability for greater control over implementations. Features include extended thinking with tool use, parallel tool execution, improved instruction following, and significantly reduced shortcut behaviors.

Claude Sonnet 4

Improved across key benchmarks for reasoning, multimodality, code and long context while getting even more efficient. Best for fast performance on complex tasks.

Gemini 2.5 Flash

Our state-of-the-art, experimental text diffusion model that uses diffusion techniques to explore a new kind of language model that gives users greater control, creativity, and speed in text generation.

Gemini Diffusion

Gemma 3n is a mobile-first AI model built on a groundbreaking architecture optimized for on-device performance. It features Per-Layer Embeddings (PLE) for reduced RAM usage, allowing larger models to run on mobile devices with a memory footprint comparable to much smaller models. Supports multimodal understanding including text, images, audio, and video.

Gemma 3n

The MedGemma collection contains Google's most capable open models for medical text and image comprehension, built on Gemma 3. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma comes in two variants: a 4B multimodal version and a 27B text-only version. The 4B version utilizes a SigLIP image encoder specifically pre-trained on de-identified medical data including chest X-rays, dermatology images, ophthalmology images, and histopathology slides.

MedGemma

Gemini 2.5 Pro is capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy. Features Deep Think, an enhanced reasoning mode, and native audio outputs that capture subtle nuances of speech.

Gemini 2.5 Pro

Third-generation Qwen model featuring hybrid reasoning capabilities that can switch between thinking and non-thinking modes. Trained on 36 trillion tokens (double that of Qwen2.5), with support for 119 languages and dialects. Available in 6 dense models (0.6B to 32B parameters) and 2 MoE models (30B/3B active and 235B/22B active).

Qwen-3

OpenAI's most powerful reasoning model that pushes the frontier across coding, math, science, visual perception, and more. Sets new state-of-the-art on benchmarks including Codeforces, SWE-bench, and MMMU. Features full tool access and can agentically use and combine every tool within ChatGPT.

A smaller model optimized for fast, cost-efficient reasoning. Achieves remarkable performance for its size and cost, particularly in math, coding, and visual tasks. It is the best-performing benchmarked model on AIME 2024 and 2025, with significantly higher usage limits than o3.

o4-mini

Latest Meta AI model, at ~400B parameters and trained on 40T tokens. Intended to push open-model performance to near-GPT-4 level. Released under a new Llama 4 community license. Early indications show significant gains in reasoning and multilingual understanding.

Llama 4 (400B)

Smaller version of GPT-4.1 optimized for coding and instruction following while maintaining the 1M token context window. Designed for applications requiring strong coding capabilities with better efficiency.

GPT-4.1 mini

Most compact version of GPT-4.1 family, optimized for efficiency while maintaining strong coding and instruction following capabilities. Designed for applications requiring low latency and cost.

GPT-4.1 nano

Latest generation GPT model with massive 1M token context window and significant improvements in coding, reasoning, and multimodal understanding. Sets new state-of-the-art results on multiple benchmarks.

GPT-4.1

Gemma 3 is a collection of lightweight, state-of-the-art open models built from the same research and technology that powers Gemini 2.0 models. The most capable model you can run on a single GPU or TPU, delivering state-of-the-art performance for its size and outperforming Llama3-405B, DeepSeek-V3 and o3-mini in preliminary human preference evaluations on LMArena's leaderboard. Features advanced text and visual reasoning capabilities, 128K token context window, function calling, and support for over 140 languages.

Gemma 3

A research preview of OpenAI's largest and most capable GPT model to date. GPT-4.5 represents a significant advancement in scaling unsupervised learning, delivering broader world knowledge, dramatically reduced hallucinations (37.1% vs 61.8% for GPT-4o), and enhanced emotional intelligence for more natural human collaboration. Excels at creative tasks, writing assistance, and complex problem-solving with improved factual accuracy and nuanced understanding of user intent.

GPT-4.5

Best for cost-efficient performance. Better quality than 1.5 Flash, at the same speed and cost. Features a 1 million token context window and multimodal input.

Gemini 2.0 Flash-Lite

Next iteration of Gemini, released in three versions (Flash, Flash-Lite, Pro). Represents Google's state-of-the-art multimodal model, likely incorporating Mixture-of-Experts at unprecedented scale and targeting dominance in both text and image understanding.

Gemini 2.0 Flash

Anthropic's most intelligent model to date and the first hybrid reasoning model on the market. Claude 3.7 Sonnet can produce near-instant responses or extended, step-by-step thinking that is made visible to the user. API users have fine-grained control over how long the model can think for (up to 128K tokens). Priced at $3 per million input tokens and $15 per million output tokens at launch, including thinking tokens.

Claude 3.7 Sonnet

Google's best model yet for coding performance and complex prompts, with better understanding and reasoning of world knowledge than any previous release. Features a massive 2 million token context window and the ability to call tools like Google Search and code execution.

Gemini 2.0 Pro

Third iteration of xAI's model, purportedly trained with 10× the compute of previous state-of-the-art models. Details like parameter count are unreleased (rumored multi-trillion). It represents xAI's push towards the frontier of LLM capabilities, presumably integrated into the Grok service.

Grok 3

DeepSeek-R1 is a first-generation reasoning model trained via large-scale reinforcement learning. Built on DeepSeek-V3-Base, it incorporates cold-start data before RL to address challenges like endless repetition and poor readability found in DeepSeek-R1-Zero. Achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks through advanced chain-of-thought reasoning capabilities.

DeepSeek-R1

A powerful Mixture-of-Experts (MoE) language model with 671B total parameters and 37B activated for each token. Features Multi-head Latent Attention (MLA) and DeepSeekMoE architectures with innovative auxiliary-loss-free load balancing and multi-token prediction training. Pre-trained on 14.8T high-quality tokens with only 2.788M H800 GPU hours.

DeepSeek-V3

Family of models (Nova Micro, Lite, Pro) launched as part of AWS's Bedrock services. Details are limited, but these models power Amazon's own generative AI offerings, similar to how Azure uses OpenAI models.

Amazon Nova (Pro)

Flagship 123B-parameter model from Mistral AI, progressively upgraded through 2024 versions. Targeted to be a top-performing open model competing with Meta's Llama family on knowledge and reasoning tasks.

Mistral Large (123B)

A 123B-parameter **multimodal** model from Mistral, combining language with vision ("Pix-"). A 12B smaller version was released under Apache 2.0. Pixtral extends Mistral's LLM capability to image understanding and description tasks.

Pixtral 123B

Anthropics fastest model at time of launch, delivering intelligence at blazing speeds. Claude 3.5 Haiku is optimized for quick responses while maintaining high quality.

Claude 3.5 Haiku

Faster, cost-efficient reasoning model optimized for coding and agentic applications. 80% cheaper than o1-preview with strong capabilities for complex problem-solving in tasks where context is provided within the prompt.

o1-mini

Large reasoning model with strong capabilities for solving hard problems through extended thinking. Designed to spend more time reasoning before responding, with enhanced performance in science, coding, and math.

o1-preview

The o1 model series is trained with large-scale reinforcement learning to reason using chain-of-thought. OpenAI o1 is the next model in this series (previously OpenAI o1-preview), with advanced reasoning capabilities that provide new avenues for improving safety and robustness. The model can reason about safety policies in context when responding to potentially unsafe prompts, through deliberative alignment.

Third-generation Llama model, reportedly 405B parameters with 15.6T tokens of training. Introduced a massive context window (128k tokens) and expanded multilingual capability. At release, it was the largest openly available model to date.

Llama 3.1 (405B)

Claude 3.5 Sonnet raises the industry bar for intelligence, outperforming competitor models and Claude 3 Opus on a wide range of evaluations, with the speed and cost of a mid-tier model. It operates at twice the speed of Claude 3 Opus.

Claude 3.5 Sonnet

Second-generation Qwen model (72B parameters) open-sourced under Alibaba's Qianwen License. Trained on 3T tokens; released alongside smaller variants and intended as foundation for Alibaba's enterprise AI services.

Qwen-2

GPT-4o ('o' for 'omni') is a multimodal model that accepts any combination of text, audio, image, and video inputs and generates text, audio, and image outputs. It matches GPT-4 Turbo performance on English text and code, with significant improvements for non-English languages. It responds to audio inputs in as little as 232 milliseconds (avg. 320ms), similar to human conversation response time. GPT-4o is 50% cheaper in the API than previous models and features superior vision and audio understanding capabilities.

GPT-4o

14B-parameter follow-up to Phi-1/2 focusing on compact "small models" with high-quality training. Released in sizes 3B, 7B, 14B. Emphasizes efficient training and inference.

Phi-3

Anthropic's fastest and most compact model at the time of launch with near-instant responsiveness. Answers simple queries and requests with unmatched speed while maintaining good quality. Pricing at launch: $0.25 per million input tokens, $1.25 per million output tokens.

Claude 3 Haiku

Anthropic's most intelligent model at release, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts with remarkable fluency and human-like understanding. Pricing at launch: $15 per million input tokens, $75 per million output tokens.

Claude 3 Opus

Anthropic's balanced model that strikes an ideal balance between intelligence and speed for enterprise workloads. Delivers strong performance at a lower cost compared to its peers. Pricing at launch: $3 per million input tokens, $15 per million output tokens.

Claude 3 Sonnet

Upgrade over Gemini 1.0, featuring a **1 million+ token context window** (initially 1M, later expanded to 2M). Likely uses a Mixture-of-Experts backend and improved multimodal abilities. Released in variants (e.g. Flash).

Gemini 1.5

First version of Google's Gemini, a multimodal model built by the joint Google DeepMind team. Comes in three sizes; integrated into a chatbot (Google's next-gen model after PaLM 2) with planned image and text capabilities.

Gemini 1.0

2.7B-parameter model focused on efficiency and quality by training on high-quality (including synthetic) data. Achieved surprisingly strong results for its size, dubbed a "textbook-quality" model.

Phi-2

A sparse Mixture-of-Experts model (8 experts) with a total of 46.7B parameters (12.9B active per token). Outperformed dense models like Llama 2 70B and GPT-3.5 on many benchmarks at significantly lower compute cost.

Mixtral 8×7B

Incremental update to Claude 2 with an expanded 200K token context window. Allowed analysis of very large documents (~500 pages) in a single prompt.

Claude 2.1

DeepSeek LLM is an advanced language model comprising 67 billion parameters, trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. The model demonstrates superior general capabilities, outperforming Llama2 70B Base in reasoning, coding, math, and Chinese comprehension. Available in both Base and Chat variants with 7B and 67B parameter sizes.

DeepSeek LLM

Improved version of GPT-4 with significantly expanded context window, more recent knowledge cutoff, and optimized performance. Designed for production applications with better cost efficiency than the base GPT-4.

GPT-4 Turbo

First model from Elon Musk's xAI, reportedly with 314B parameters. Deployed in the Grok chatbot with real-time access to X (Twitter) data. Offered an 8K context and aimed to provide up-to-date information via integration with social media.

Grok 1

7.3B-parameter model trained from scratch with improved data and architecture enhancements. Open-source (Apache 2.0) with strong performance that rivaled larger 13B+ models on many benchmarks.

Mistral 7B

Anthropic's second-generation model, offering improved reasoning, coding, and a much larger 100K token context window. Available via API and chatbot, it scored highly on knowledge and coding benchmarks while maintaining constitutional AI alignment.

Claude 2

70B-parameter successor to LLaMA, trained on 2 trillion tokens and fine-tuned for helpful dialogue. Released openly under a custom license allowing commercial use with some restrictions. Strong performance rivaling closed models like ChatGPT on many benchmarks.

Llama 2 70B

Extended context version of GPT-3.5 Turbo with a 16K token context window. Optimized for chat applications with improved instruction following and reduced hallucinations compared to base GPT-3.5.

GPT-3.5 Turbo 16K

Second-generation PaLM, actually a family of models (up to ~340B dense params) with training on a diverse multilingual dataset. Powers Google's Bard and other products; improved coding, reasoning, and multilingual skills over PaLM.

PaLM 2

Fourth-generation GPT model, with greatly improved reasoning and multimodal capabilities (accepts text and images). Model size not publicly disclosed (rumored ~1.7 trillion parameters). Achieved human-level performance on many academic and professional benchmarks.

GPT-4

Large Language Model Meta AI, released in sizes up to 65B parameters for research. Achieved strong performance by training on 1.4T tokens; weights were leaked, spurring a wave of fine-tuned variants.

LLaMA 65B

20B-parameter seq2seq model (teacher-student ensemble) for powering Amazon Alexa's multilingual understanding. Uses a bidirectional encoder-decoder architecture for translation and dialogue tasks.

AlexaTM 20B

120B-parameter model aimed at scientific text and knowledge integration. Notorious for generating plausible-sounding but incorrect scientific content; public demo was retracted.

Galactica

OpenAI's public conversational AI based on GPT-3.5. Fine-tuned for dialogue via RLHF, leading to widespread adoption in late 2022. Marked the first widely accessible GPT-powered chatbot.

GPT-3.5 Turbo

Open Pretrained Transformer, a reproduction of GPT-3 (175B) made openly available to researchers. Notably released with training logs and a non-commercial license.

OPT 175B

540B-parameter Transformer model trained with the Pathways system. Showed strong few-shot performance and was the largest dense model publicly disclosed as of 2022.

PaLM

70B-parameter model trained on 4× more data than typical, to be compute-optimal. Achieved superior accuracy vs much larger models, validating data-scaling laws.

Chinchilla

137B-parameter dialogue-optimized language model ("Language Model for Dialogue Applications"). Known for conversational ability and for sparking discussions on AI sentience.

LaMDA

Instruction-tuned version of GPT-3 (a.k.a GPT-3.5) refined via Reinforcement Learning from Human Feedback (RLHF). Powers the initial version of ChatGPT (Dec 2022). Optimized for following user instructions safely.

InstructGPT/GPT-3.5

Anthropic's first large model (52B params) tuned with Constitutional AI for helpful and harmless dialogue. Initially deployed to limited users in early 2022.

Claude v1 (52B)

A 1.2 trillion-parameter **Mixture-of-Experts** Transformer model. Uses sparse activation (64 experts) to reduce inference cost relative to model size.

GLaM (Generalist Language Model)

A 280B-parameter Transformer model from DeepMind, used to explore scaling laws and as a precursor to Chinchilla.

Gopher

A 530-billion-parameter Transformer language model, one of the largest dense LLMs of 2021. Collaboration between NVIDIA and Microsoft, used for research on scaling.

Megatron-Turing NLG 530B

A GPT-3-descendent model fine-tuned on billions of lines of source code from publicly available sources, including code in public GitHub repositories. Powers GitHub Copilot for code autocompletion and demonstrated strong coding ability (solving ~28% of HumanEval at 12B params). Most capable in Python, but proficient in over a dozen languages including JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, and Shell.

Codex

175B-parameter GPT series model that set new benchmarks in few-shot learning and text generation. Demonstrated impressive zero-shot capabilities across NLP tasks.

GPT-3

Encoder-decoder Transformer trained on a unified text-to-text format for diverse NLP tasks. Enabled prompt-based multitask learning.

T5 (Text-To-Text Transfer Transformer)

Autoregressive Transformer that uses permuted language modeling as an alternative to BERT. Achieved state-of-the-art on several NLP tasks in 2019.

XLNet

Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. It was pre-trained on a dataset of 8 million web pages (WebText) and was a direct scale-up of GPT-1 with a ten-fold increase in both parameter count and training dataset size. Initially partially withheld due to concerns about potential misuse, the full model was released in November 2019.

GPT-2

Bidirectional Transformer pre-trained for language understanding (not generative). Influential for NLP tasks with masked-language modeling.

BERT

Generative Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models, demonstrating unsupervised pre-training with a 12-layer decoder-only Transformer. It introduced a two-stage training process leveraging the BookCorpus dataset and achieved improvements across various NLP tasks.

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