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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.
ControlNet (SD 3.5 Large) Depth
🇺🇸 Stability AI
ConfRank
🇩🇪 University of Bonn
Hymba
🇺🇸 NVIDIA
Tulu 3 (Tülu 3) 70B
🇺🇸 Allen Institute for AI
Tulu 3 8B
🇺🇸 Allen Institute for AI
DeepSeek-R1-Lite-Preview
🇨🇳 DeepSeek
Boltz-1
🇺🇸 Massachusetts Institute of Technology (MIT)
360Zhinao2-7B
🇨🇳 360 Security Technology
BiRNA-BERT
🇺🇸 Bangladesh University of Engineering and Technology
SK Telecom Telco
🇰🇷 SK Telecom
k0-math
🇨🇳 Moonshot
Gemini-Exp-1114
🇺🇸 Google DeepMind
LLaVA-CoT
🇨🇳 Peking University
Mistral Large 2.1
🇫🇷 Mistral AI
Qwen2.5-Turbo
🇨🇳 Qwen
bailing-pro-1120
🇨🇳 Ant Group
Athene-V2
🇺🇸 Nexusflow
Gemma2 9B CPT Sahabat-AI
🇮🇳 Indosat
Qwen2.5-Coder (32B)
🇨🇳 Qwen
MassiveFold
🇫🇷 Université de Lille
stFormer
🇺🇸 Shanghai Jiao Tong University
TeleChat2-3B
🇨🇳 China Telecom
TeleChat2-7B
🇨🇳 China Telecom
Mistral Moderation
🇫🇷 Mistral AI
FLUX1.1 [pro] Raw
🇩🇪 Black Forest Labs
FLUX1.1 [pro] Ultra
🇩🇪 Black Forest Labs
Hunyuan-Large
🇨🇳 Tencent
OpenPhenom-S/16
🇺🇸 Recursion Pharmaceuticals
Llama-3.1-Minitron-4B
🇺🇸 NVIDIA
Minitron 4B
🇺🇸 NVIDIA
Minitron 8B
🇺🇸 NVIDIA
KwooLa
🇨🇳 Harbin Institute of Technology
FvFold
🇰🇷 Jeonbuk National University
Lara V1
🇮🇹 Translated
Minerva 7B
🇮🇹 Sapienza NLP
SimPO
🇺🇸 Princeton University
AstroOne
🇨🇳 Zhijiang Lab
Recraft V3
🇬🇧 Recraft
aiXcoder-7B Base
🇨🇳 Peking University
Haiper 2.0
🇬🇧 Haiper
Stable Diffusion 3.5 Medium
🇺🇸 Stability AI
Doubao-pro
🇨🇳 ByteDance
Pro-PRIME
🇺🇸 Shanghai Jiao Tong University
Aya Expanse 32B
🇨🇦 Cohere for AI
Aya Expanse 8B
🇨🇦 Cohere for AI
Bielik 7B
🇵🇱 SpeakLeash
GigaChat MAX
🇷🇺 Sber
YandexGPT 4 Lite
🇷🇺 Yandex
YandexGPT 4 Pro
🇷🇺 Yandex
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
Depth Anything V2 Giant
🇨🇳 Tik Tok
Depth Anything V2 Large
🇨🇳 Tik Tok
TeleChat2-35B
🇨🇳 China Telecom
Yi-Lightning
🇨🇳 01.AI
Janus 1.3B
🇨🇳 DeepSeek
Marco-o1
🇨🇳 Alibaba
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.