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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.
XY-LENTXL
🇺🇸 Microsoft
Tk-Instruct
🇺🇸 University of Washington
Verbatim Memory Transformer (108M)
🇺🇸 Johns Hopkins University
Verbatim Memory Transformer (117M)
🇺🇸 Johns Hopkins University
Flan-PaLM 540B
Flan-T5 11B
LMSI-Palm
U-PaLM (540B)
GenSLM
🇺🇸 University of Chicago
Instruct-GPT + Mind's Eye
DiffDock
🇺🇸 Massachusetts Institute of Technology (MIT)
AminoBert
🇺🇸 Harvard Medical School
NMST+GPT-2
🇺🇸 New York University (NYU)
GemNet-OC
🇺🇸 Technical University of Munich
CPAC
🇺🇸 Texas A&M
DistilProtBert
🇺🇸 Bar-Ilan University
NeMO Megatron GPT 20B
🇺🇸 NVIDIA
ProteinMPNN
🇺🇸 University of Washington
PaLI
Stable Diffusion 1.5
🇺🇸 Runway
M3GNet
🇺🇸 University of California San Diego
BlenderBot 3
🇺🇸 McGill University
RNA-FM
🇺🇸 Chinese University of Hong Kong (CUHK)
AlexaTM 20B
🇺🇸 Amazon
OmegaPLM
🇺🇸 Massachusetts Institute of Technology (MIT)
ESM2-150M
🇺🇸 Meta AI
ESM2-15B
🇺🇸 Meta AI
ESM2-35M
🇺🇸 Meta AI
ESM2-3B
🇺🇸 Meta AI
ESM2-650M
🇺🇸 Meta AI
ESM2-8M
🇺🇸 Meta AI
Delphi
🇺🇸 Allen Institute for AI
BLOOM-176B
🇺🇸 Hugging Face
NLLB
🇺🇸 Meta AI
BLOOM-1.7B
🇺🇸 Hugging Face
BLOOM-1B
🇺🇸 Hugging Face
BLOOM-3B
🇺🇸 Hugging Face
BLOOM-560M
🇺🇸 Hugging Face
BLOOM-7.1B
🇺🇸 Hugging Face
CodeT5-large
🇺🇸 Salesforce
WebGPT
🇺🇸 OpenAI
Minerva (540B)
DALL-E mega
🇺🇸 Craiyon
ProGen2-base
🇺🇸 Salesforce Research
ProGen2-xlarge
🇺🇸 Salesforce Research
CodeWhisperer
🇺🇸 Amazon
Parti
🇺🇸 Google Research
OPT-1.3B
🇺🇸 Meta AI
OPT-1.3B (finetuned on PTB)
🇺🇸 Meta AI
OPT-1.3B (finetuned)
🇺🇸 Meta AI
OPT-125M (finetuned on PTB)
🇺🇸 Meta AI
OPT-125M (finetuned)
🇺🇸 Meta AI
OPT-2.7B
🇺🇸 Meta AI
OPT-2.7B (finetuned on PTB)
🇺🇸 Meta AI
OPT-2.7B (finetuned on WT2)
🇺🇸 Meta AI
OPT-30B
🇺🇸 Meta AI
OPT-350M
🇺🇸 Meta AI
OPT-6.7B
🇺🇸 Meta AI
OPT-66B
🇺🇸 Meta AI
BIG-G 137B
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.