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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 โ GPT, Claude, Gemini, Llama, Mistral, Qwen and 400+ more โ with parameter counts, context windows, benchmark scores and provider pricing.
Stable Diffusion 1.2
๐ฉ๐ช Ludwig Maximilian University of Munich
Stable Diffusion 1.4
๐ฉ๐ช Ludwig Maximilian University of Munich
Stable Diffusion 1.5
๐บ๐ธ Runway
Dream Diary (้ ๆขฆๆฅ่ฎฐ)
๐จ๐ณ West Lake Xinchen / Xinchen AI / ่ฅฟๆนๅฟ่พฐ๏ผๆญๅท๏ผ็งๆๆ้ๅ ฌๅธ
Luminous-base
๐ฉ๐ช Aleph Alpha
Luminous-extended
๐ฉ๐ช Aleph Alpha
Luminous-supreme
๐ฉ๐ช Aleph Alpha
PeTriBERT
๐ซ๐ท University of Montpellier
M3GNet
๐บ๐ธ University of California San Diego
BlenderBot 3
๐บ๐ธ McGill University
RNA-FM
๐บ๐ธ Chinese University of Hong Kong (CUHK)
SGPT BE 5.8B
๐จ๐ณ Peking University
GLM-130B
๐จ๐ณ Tsinghua University
AlexaTM 20B
๐บ๐ธ Amazon
GPT-NeoX-Japanese
๐ฏ๐ต Abeja
ProtGPT2
๐ฉ๐ช University of Bayreuth
OmegaPLM
๐บ๐ธ Massachusetts Institute of Technology (MIT)
ESM2-150M
๐บ๐ธ Meta AI
ESM2-15B
๐บ๐ธ Meta AI
ESM2-35M
๐บ๐ธ Meta AI
ESM2-3B
๐บ๐ธ Meta AI
ESM2-650M
๐บ๐ธ Meta AI
ESM2-8M
๐บ๐ธ Meta AI
YuYan 11B
๐จ๐ณ Hong Kong Baptist University
Rita-XLarge
๐ซ๐ท LightOn
Transformer-XL + RMT
๐ท๐บ Moscow Institute of Physics and Technology
Delphi
๐บ๐ธ Allen Institute for AI
BLOOM-176B
๐บ๐ธ Hugging Face
NLLB
๐บ๐ธ Meta AI
BLOOM-1.7B
๐บ๐ธ Hugging Face
BLOOM-1B
๐บ๐ธ Hugging Face
BLOOM-3B
๐บ๐ธ Hugging Face
BLOOM-560M
๐บ๐ธ Hugging Face
BLOOM-7.1B
๐บ๐ธ Hugging Face
CodeT5-large
๐บ๐ธ Salesforce
WebGPT
๐บ๐ธ OpenAI
Drahim PFM AI
๐ธ๐ฆ Drahim
Minerva (540B)
๐บ๐ธ Google
DALL-E mega
๐บ๐ธ Craiyon
ProGen2-base
๐บ๐ธ Salesforce Research
ProGen2-xlarge
๐บ๐ธ Salesforce Research
GPT-SW3
๐ธ๐ช AI Sweden
CodeWhisperer
๐บ๐ธ Amazon
YaLM
๐ท๐บ Yandex
CodeGeeX
๐จ๐ณ Zhipu AI
Parti
๐บ๐ธ Google Research
OPT-1.3B
๐บ๐ธ Meta AI
OPT-1.3B (finetuned on PTB)
๐บ๐ธ Meta AI
OPT-1.3B (finetuned)
๐บ๐ธ Meta AI
OPT-125M (finetuned on PTB)
๐บ๐ธ Meta AI
OPT-125M (finetuned)
๐บ๐ธ Meta AI
OPT-2.7B
๐บ๐ธ Meta AI
OPT-2.7B (finetuned on PTB)
๐บ๐ธ Meta AI
OPT-2.7B (finetuned on WT2)
๐บ๐ธ Meta AI
OPT-30B
๐บ๐ธ Meta AI
OPT-350M
๐บ๐ธ Meta AI
OPT-6.7B
๐บ๐ธ Meta AI
OPT-66B
๐บ๐ธ Meta AI
EGRU (PTB)
๐ฌ๐ง Ruhr University Bochum
EGRU (WT2)
๐ฌ๐ง Ruhr University Bochum
About Large language models (LLMs)
Large language models (LLMs) are the foundation of modern generative AI โ 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 โ 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 โ 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.