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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.
Weblab-10B
🇯🇵 Matsuo Lab
BELLE-Llama2-13B-chat-0.4M
🇨🇳 KE Holdings Inc. (“Beike”)
YAYI-13B-Llama2
🇨🇳 Yayi (Wenge)
YAYI-7B-Llama2
🇨🇳 Yayi (Wenge)
Stable Beluga 1
🇺🇸 Stability AI
Stable Beluga 2
🇺🇸 Stability AI
Pangu-Weather
🇨🇳 Huawei
Stable Diffusion XL (SDXL)
🇺🇸 Stability AI
Hermes 7B ITA
🇮🇹 RAI
BELLE-LLaMA-13B-2M-enc
🇨🇳 KE Holdings Inc. (“Beike”)
BELLE-LLaMA-7B-0.6M-enc
🇨🇳 KE Holdings Inc. (“Beike”)
BELLE-LLaMA-7B-2M-enc
🇨🇳 KE Holdings Inc. (“Beike”)
Baichuan1-7B
🇨🇳 Baichuan
Fauno
🇮🇹 Sapienza NLP
MASSA
🇨🇳 Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems
30B-Lazarus
Caldera AI
Goat-7B
🇨🇳 National University of Singapore
Guanaco-65B
🇺🇸 University of Washington
CoEdiT-xxl
🇺🇸 University of Minnesota
WizardLM-7B
🇺🇸 Microsoft
BELLE-LLaMA-EXT-7B
🇨🇳 KE Holdings Inc. (“Beike”)
HuaTuo
🇨🇳 Harbin Institute of Technology
Incoder-6.7B
🇺🇸 Facebook AI Research
gLM
🇺🇸 Harvard University
Alpaca
🇺🇸 Stanford University
DiT-XL/2
🇺🇸 New York University (NYU)
LLaMA-13B
🇺🇸 Meta AI
LLaMA-33B
🇺🇸 Meta AI
LLaMA-65B
🇺🇸 Meta AI
LLaMA-7B
🇺🇸 Meta AI
Ankh_base
🇺🇸 Technical University of Munich
Ankh_large
🇺🇸 Technical University of Munich
Nucleotide Transformer
🇺🇸 NVIDIA
BART-IT
🇮🇹 Politecnico di Torino
OPT-IML (175B)
🇺🇸 Meta AI
Discriminator Guidance
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
Galactica
🇺🇸 Meta AI
CPM-Ant
🇨🇳 Tsinghua University
BlenderBot 3
🇺🇸 McGill University
SGPT BE 5.8B
🇨🇳 Peking University
GLM-130B
🇨🇳 Tsinghua University
OPT-1.3B
🇺🇸 Meta AI
OPT-2.7B
🇺🇸 Meta AI
OPT-30B
🇺🇸 Meta AI
OPT-350M
🇺🇸 Meta AI
OPT-6.7B
🇺🇸 Meta AI
OPT-66B
🇺🇸 Meta AI
TRIMELMext (247M)
🇺🇸 Princeton University
TRIMELMext (7M)
🇺🇸 Princeton University
TRIMELMlong (150M)
🇺🇸 Princeton University
OPT-175B
🇺🇸 Meta AI
BERT-RBP
🇯🇵 Waseda University
MoE-1.1T
🇺🇸 Meta AI
XGLM-7.5B
🇺🇸 Meta AI
Contriever
🇺🇸 Meta AI
KoGPT
🇰🇷 Kakao
LM-GVP
🇺🇸 Amazon Machine Learning Solutions Lab
StyleGAN3-R
🇺🇸 NVIDIA
StyleGAN3-T
🇺🇸 NVIDIA
ADM
🇺🇸 OpenAI
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.