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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.
GPT-4o mini
🇺🇸 OpenAI
Cygnet
🇺🇸 Gray Swan
MP4
🇺🇸 310.ai
MAP-Neo
🇺🇸 University of Waterloo
OpenDiLoCo 1.1B
🇺🇸 Prime Intellect
OpenDiLoCo 150M
🇺🇸 Prime Intellect
Precious3GPT
🇺🇸 Insilico Medicine AI
DualNetGO
🇺🇸 Hong Kong University of Science and Technology (HKUST)
Smaug-34B
🇺🇸 Abacus AI
Smaug-72B
🇺🇸 Abacus AI
CriticGPT
🇺🇸 OpenAI
Molecular Diffusion Models with Virtual Receptors
🇺🇸 Verily Research
ESM3 (98B)
🇺🇸 EvolutionaryScale
ESM3-open-small
🇺🇸 EvolutionaryScale
BADGER
🇺🇸 NVIDIA
Gemma 2 27B
🇺🇸 Google DeepMind
Gemma 2 2B
🇺🇸 Google DeepMind
Gemma 2 9B
🇺🇸 Google DeepMind
Code Droid
🇺🇸 Factory
Hermes 2 Theta Llama-3 70B
🇺🇸 Nous Research
JIUTIAN-139MoE
🇺🇸 China Mobile
MindEye2
🇺🇸 Stability AI
Nemotron-4 340B
🇺🇸 NVIDIA
Llama-3.1-Nemotron-70B-Instruct
🇺🇸 NVIDIA
Mamba2-Hybrid
🇺🇸 NVIDIA
ProteinReDiff
🇺🇸 FPT Software AI Center
Shutterstock ImageAI
🇺🇸 Databricks
Stable Diffusion 3 Medium
🇺🇸 Stability AI
Samba 3.8B
🇺🇸 Microsoft
Prot2Token
🇺🇸 University of Missouri
DRGN-AI
🇺🇸 Stanford University
Granite 20B
🇺🇸 IBM Research
Mamba 2, 2.7B
🇺🇸 Princeton University
Aurora
🇺🇸 Microsoft Research
NV-Embed-v1
🇺🇸 NVIDIA
TimeGPT-1
🇺🇸 Nixtla
Zamba2-7B
🇺🇸 Zyphra
Genie 2 (bio)
🇺🇸 Columbia University
OMNI-EPIC
🇺🇸 Imperial College London
ProSST
🇺🇸 Shanghai Jiao Tong University
LBSTER
🇺🇸 Prescient Design
BRIA 2.3
🇺🇸 BRIA AI
Imagen 3
🇺🇸 Google DeepMind
LearnLM-Tutor
🇺🇸 Google Research
MoLeR
🇺🇸 Microsoft Research
MatterSim (Grpaphomer)
🇺🇸 Microsoft Research AI for Science
MatterSim (M3GNet - MatterSim-v1.0.0-5M)
🇺🇸 Microsoft Research AI for Science
AlphaFold 3
🇺🇸 Google DeepMind
BiosimDock
🇺🇸 DeepOrigin
Amazon Titan Text Premier
🇺🇸 Amazon
Med-Gemini-2D
🇺🇸 Google DeepMind
Med-Gemini-3D
🇺🇸 Google DeepMind
Microsoft MAI-1
🇺🇸 Microsoft
OpenELM-1.1B
🇺🇸 Apple
OpenELM-270M
🇺🇸 Apple
OpenELM-3B
🇺🇸 Apple
OpenELM-450M
🇺🇸 Apple
GenCast
🇺🇸 Google DeepMind
Med-Gemini-M 1.5
🇺🇸 Google DeepMind
Amazon Q Developer
🇺🇸 Amazon
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.