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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.
Phi-4 Mini
๐บ๐ธ Microsoft
Spark-X1
๐จ๐ณ iFlytek
Image-01
๐จ๐ณ MiniMax
GPT-4.5
๐บ๐ธ OpenAI
Kimi 1.6
๐จ๐ณ Moonshot
Mercury
๐บ๐ธ Inception Labs
Euler (Eulerๅคงๆจกๅ)
Unknown
Granite 3.2
๐บ๐ธ IBM
Granite 3.2 2B
๐บ๐ธ IBM
Granite 3.2 8B
๐บ๐ธ IBM
Granite Guardian 3.2
๐บ๐ธ IBM
BFS-Prover
๐จ๐ณ ByteDance
Bailing-Pro-20250225
๐จ๐ณ Ant Group
Tianxi-7B
๐จ๐ณ Lenovo
YandexGPT 5 Lite
๐ท๐บ Yandex
YandexGPT 5 Pro
๐ท๐บ Yandex
gemini-2.0-flash-lite
๐บ๐ธ Google DeepMind
Claude 3.7 Sonnet
๐บ๐ธ Anthropic
Claude Sonnet 3.7
๐บ๐ธ Anthropic
Evo 2 40B
๐บ๐ธ Arc Institute
Evo 2 7B
๐บ๐ธ Arc Institute
Grok 3 Beta
๐บ๐ธ xAI
Grok-3 mini
๐บ๐ธ xAI
Qwen2.5-VL-3B
๐จ๐ณ Qwen
Qwen2.5-VL-72B
๐จ๐ณ Qwen
Qwen2.5-VL-7B
๐จ๐ณ Qwen
Brain2Qwerty
๐บ๐ธ Meta AI
R1 1776
๐บ๐ธ Perplexity
Step-1
๐จ๐ณ StepFun
Grok 3
๐บ๐ธ xAI
Mistral Saba
๐ซ๐ท Mistral AI
Vitruvian-1
๐ฎ๐น ASC27
BRIA 3.1
๐บ๐ธ BRIA AI
Deephermes 3 Llama 3 8B Preview
๐บ๐ธ Nous Research
Granite Vision 3.2 2B
๐บ๐ธ IBM
LLaDA
๐จ๐ณ Renmin University of China
Sonar Deep Research
๐บ๐ธ Perplexity
Sonar
๐บ๐ธ Perplexity
Velvet 2B
๐ฎ๐น Almawave
Hunyuan Standard
๐จ๐ณ Tencent
OREAL 32B
๐จ๐ณ Shanghai AI Lab
OREAL 7B
๐จ๐ณ Shanghai AI Lab
Eurus-2-7B-PRIME
๐จ๐ณ Tsinghua University
Prithvi-EO-2.0 300M
๐บ๐ธ IBM Research
Prithvi-EO-2.0 600M
๐บ๐ธ IBM Research
EngGPT2 16B-A3B
๐ฎ๐น Engineering Ingegneria Informatica
Phi-4-mini-instruct
๐บ๐ธ Microsoft
Velvet 14B
๐ฎ๐น Almawave
o3-mini
๐บ๐ธ OpenAI
s1-32B
๐บ๐ธ Stanford University
s1.1
๐บ๐ธ Stanford University
Mistral Small 3
๐ซ๐ท Mistral AI
Tulu 3 405B
๐บ๐ธ Allen Institute for AI
Sonar Reasoning
๐บ๐ธ Perplexity
Qwen2.5-Max
๐จ๐ณ Qwen
Janus-Pro-1B
๐จ๐ณ DeepSeek
Janus-Pro-7B
๐จ๐ณ DeepSeek
Sonar Pro
๐บ๐ธ Perplexity
DoMINO
๐บ๐ธ NVIDIA
DeepSeek-R1-Distill-Llama-70B
๐จ๐ณ DeepSeek
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.