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Large language models (LLMs)
Browse every major large language model in one place. This LLM list tracks frontier and open-source foundation models — GPT, Claude, Gemini, Llama, Mistral, Qwen and 400+ more — with parameter counts, context windows, benchmark scores and provider pricing.
Multi-Token Prediction 13B
🇺🇸 Facebook AI Research
Multi-Token Prediction 7B
🇺🇸 Facebook AI Research
Arctic
🇺🇸 Snowflake
Firefly Image 3
🇺🇸 Adobe
Phi-3.5-MoE
🇺🇸 Microsoft
SI-PLM
🇺🇸 University of Pittsburgh
phi-3-medium 14B
🇺🇸 Microsoft
phi-3-mini 3.8B
🇺🇸 Microsoft
phi-3-small 7.4B
🇺🇸 Microsoft
phi-3.5-mini
🇺🇸 Microsoft
VISTA-2D
🇺🇸 NVIDIA
SaProt
🇺🇸 Zhejiang University (ZJU)
LLaMA-3-Instruct-8B
🇺🇸 Meta AI
Llama 3-70B
🇺🇸 Meta AI
Llama 3-8B
🇺🇸 Meta AI
GRITLM 7B
🇺🇸 Contextual AI
GRITLM 8x7B
🇺🇸 Contextual AI
METL-Global
🇺🇸 University of Wisconsin Madison
WizardLM-2 70B
🇺🇸 Microsoft
WizardLM-2 7B
🇺🇸 Microsoft
WizardLM-2 8x22B
🇺🇸 Microsoft
DDIM
🇺🇸 University Paris-Saclay
DDPM
🇺🇸 University Paris-Saclay
AF2RAVE
🇺🇸 University of Maryland
Zephyr 141B-A39B
🇺🇸 Hugging Face
SambaLingo-Thai-Chat (7B)
🇺🇸 "SambaNova Systems
SambaLingo-Thai-Chat-70B
🇺🇸 "SambaNova Systems
Stable LM 2 12B
🇺🇸 Stability AI
eFold
🇺🇸 Harvard Medical School
AutoDiff
🇺🇸 Galixir Technologies
Mixture-of-Depths
🇺🇸 Google DeepMind
ReALM
🇺🇸 Apple
Grok-1.5
🇺🇸 xAI
DBRX
🇺🇸 Databricks
BindDM
🇺🇸 Peng Cheng Laboratory
ProstT5
🇺🇸 Technical University of Munich
MiniGPT4 + LRV-Instruction
🇺🇸 University of Maryland
ERNIE-RNA
🇺🇸 Microsoft Research
Quiet-STaR
🇺🇸 Stanford University
Inflection-2.5
🇺🇸 Inflection AI
Claude 3 Haiku
🇺🇸 Anthropic
Griffin
🇺🇸 Google DeepMind
Hawk
🇺🇸 Google DeepMind
PTM-Mamba
🇺🇸 Duke University
StarCoder 2 15B
🇺🇸 Hugging Face
StarCoder 2 3B
🇺🇸 Hugging Face
StarCoder 2 7B
🇺🇸 Hugging Face
Evo
🇺🇸 Stanford University
Nemotron-4 15B
🇺🇸 NVIDIA
Playground v2.5
🇺🇸 Playground
DecompDiff
🇺🇸 University of Illinois Urbana-Champaign (UIUC)
Gemma 1.1 7B Instruct
Stable Diffusion 3
🇺🇸 Stability AI
Gemma 2B
🇺🇸 Google DeepMind
Gemma 7B
🇺🇸 Google DeepMind
Re-Dock
🇺🇸 Zhejiang University (ZJU)
Me Llama 13B
🇺🇸 Yale School of Medicine
Me Llama 70B
🇺🇸 Yale School of Medicine
Gemini 1.5 Pro
🇺🇸 Google DeepMind
PLAPT
🇺🇸 Wolfram Research
About Large language models (LLMs)
Large language models (LLMs) are the foundation of modern generative AI — general-purpose text models trained on vast corpora that can write, reason, summarise, translate and code. Choosing the best LLM is rarely about a single winner: the best AI model for one task may lag on another. Reasoning-heavy work rewards models that score well on benchmarks like MMLU, GPQA and AIME, while agentic and tool-use workloads care more about instruction following, function calling and long-context recall. When you compare LLMs, weigh raw capability against the practical constraints that decide cost and feasibility — context window, throughput, latency, licensing and price per million tokens. Open-source LLMs such as Llama, Qwen, Mistral and DeepSeek let you self-host and fine-tune, while proprietary frontier models from OpenAI, Anthropic and Google often lead on raw quality. Use our benchmarks to see where each model ranks, and put two candidates side by side with compare before you commit to a provider.
Frequently asked questions
What is the best LLM right now?
There is no single best LLM — it depends on the task. Frontier proprietary models from OpenAI, Anthropic and Google tend to lead on reasoning benchmarks, while open-source LLMs like Llama, Qwen and DeepSeek are best when you need to self-host or fine-tune. Compare candidates on the benchmarks page for your specific workload.
What is the best open source LLM?
The strongest open-source and open-weights LLMs at any given time typically come from the Llama, Qwen, Mistral and DeepSeek families. They can be downloaded, self-hosted and fine-tuned, and the top ones rival proprietary models on many benchmarks. Filter the list above by availability to see current open-weights options.
How do I compare two LLMs?
Use the compare tool to put two models side by side on parameters, context window, availability and benchmark scores, then check the benchmarks page for task-specific rankings such as MMLU, GPQA and coding scores.