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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.
Claude 3.5 Haiku
🇺🇸 Anthropic
Stable Diffusion 3.5 Large
🇺🇸 Stability AI
Stable Diffusion 3.5 Large Turbo
🇺🇸 Stability AI
Granite 3.0 2B
🇺🇸 IBM
Granite 3.0 8B
🇺🇸 IBM
CHAI-1
🇺🇸 Chai discovery
SANA 1.6B
🇺🇸 NVIDIA
PROPERMAB
🇺🇸 Regeneron
Yuel 2
🇺🇸 Pennsylvania State University
vScreenML 2.0
🇺🇸 Fox Chase Cancer Center
Deep Learning Enabled Discovery of Kinase Drug Targets in Pharos
🇺🇸 West Virginia University
HaloClass
🇺🇸 University of California Davis
ProteinChat
🇺🇸 University of California San Diego
ProCALM (Uniref9B)
🇺🇸 Profluent Bio
CLEAN-Contact
🇺🇸 Cleveland Clinic
MLDD3UTRmRRNAS
🇺🇸 Ginkgo Bioworks
EnzymeFlow
🇺🇸 McGill University
PlasmidGPT
🇺🇸 Harvard University
FragLlama
🇺🇸 YDS Pharmatech
LFM 40B
🇺🇸 Liquid
DFMDock
🇺🇸 Johns Hopkins University
DeepREAD
🇺🇸 Shape Therapeutics
AlphaChip
🇺🇸 Google DeepMind
Loop-Diffusion
🇺🇸 University of Washington
Mdgen
🇺🇸 Massachusetts Institute of Technology (MIT)
RWKV-5 (Eagle) 7B
🇺🇸 RWKV Foundation
RWKV-6 (Finch) 3B
🇺🇸 RWKV Foundation
ChemNet
🇺🇸 University of Washington
CodonMPNN
🇺🇸 Harvard Medical School
GeoAB
🇺🇸 Zhejiang University (ZJU)
KnoMol
🇺🇸 Zhejiang University (ZJU)
Molmo 72B
🇺🇸 Allen Institute for AI
PPFlow
🇺🇸 Zhejiang University (ZJU)
ProteinGenerator
🇺🇸 University of Washington
RNA-DCGen
🇺🇸 Bangladesh University of Engineering and Technology
TxGNN
🇺🇸 Harvard Medical School
AFP-Deep
🇺🇸 Nanjing University
Importance of higher-order epistasis in large protein sequence-function relationships
🇺🇸 University of Florida
Llama 3.2 1B
🇺🇸 Meta AI
Llama 3.2 3B
🇺🇸 Meta AI
MTDP
🇺🇸 Chinese University of Hong Kong (CUHK)
Protein-Mamba
🇺🇸 Rensselaer Polytechnic Institute
dnaGrinder
🇺🇸 Hong Kong Polytechnic University
AMPLIFY
🇺🇸 Chandar Research Lab
ProteinSetTransformer
🇺🇸 University of Wisconsin Madison
MoEFold2D
🇺🇸 George Washington University
Prithvi WxC
🇺🇸 IBM Research
McMLP
🇺🇸 Harvard Medical School
ProtENN2
🇺🇸 European Bioinformatics Institute
RoseTTAFold2-Lite
🇺🇸 University of Washington
AminoAcid-0
🇺🇸 Ginkgo Bioworks
DeepUrfold
🇺🇸 University of Virginia
Playground v3
🇺🇸 Playground
RNA language models predict mutations that improve RNA function
🇺🇸 "NERSC
RNAdiffusion
🇺🇸 Princeton University
MolSnapper
🇺🇸 University of Oxford
CodonTransformer
🇺🇸 Vector Institute
JURA Bio Model
🇺🇸 JURA Bio
Text2Protein
🇺🇸 University of California San Diego
DataGemma
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.