New: connect Claude & other AIs to GenAIList over MCP — research the catalog and contribute to the shared knowledge base. Learn how →
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.
AlphaEvolve
🇬🇧 DeepMind
NTele-R1-32B-V1
🇨🇳 ZTE
Earth-2 (cBottle-SR)
🇺🇸 NVIDIA
Tianxi-32B
🇨🇳 Lenovo
Tianxi-72B
🇨🇳 Lenovo
Pangu Ultra MoE
🇨🇳 Huawei
Apriel Nemotron 15B
🇺🇸 NVIDIA
Gemini 2.5 Pro (May 2025)
🇺🇸 Google DeepMind
Kevin-32B
🇺🇸 Cognition
Typhoon 2.1 Gemma 12B
🇹🇭 Typhoon / SCB 10X
Typhoon 2.1 Gemma 4B
🇹🇭 Typhoon / SCB 10X
DeepSeek-Prover-V2-671B
🇨🇳 DeepSeek
DeepSeek-Prover-V2-7B
🇨🇳 DeepSeek
GTE-ModernColBERT-v1
🇫🇷 LightOn
Phi-4-Reasoning
🇺🇸 Microsoft
Phi-4-Reasoning-plus
🇺🇸 Microsoft
TranscriptFormer
🇺🇸 Chan Zuckerberg Initiative
Qwen3-0.6B
🇨🇳 Qwen
Qwen3-1.7B
🇨🇳 Qwen
Qwen3-14B
🇨🇳 Qwen
Qwen3-235B-A22B
🇨🇳 Qwen
Qwen3-30B-A3B
🇨🇳 Qwen
Qwen3-32B
🇨🇳 Qwen
Qwen3-4B
🇨🇳 Qwen
Qwen3-8B
🇨🇳 Qwen
Foundation-sec-8b
🇺🇸 Cisco
Palmyra X5
🇺🇸 Writer
Qwen3 (series)
🇨🇳 Alibaba
HiDream-I1
🇨🇳 HiDream
Pleias-RAG-1B
🇫🇷 PleIAs
Pleias-RAG-350m
🇫🇷 PleIAs
Firefly Image 4
🇺🇸 Adobe
Firefly Image 4 Ultra
🇺🇸 Adobe
gpt-image-1
🇺🇸 OpenAI
Llama-Primus-Nemotron-70B
🇺🇸 Trend Micro
Trillion-7B
🇰🇷 Trillion Labs
Gemma 3 QAT 12B
🇺🇸 Google DeepMind
Gemma 3 QAT 1B
🇺🇸 Google DeepMind
Gemma 3 QAT 27B
🇺🇸 Google DeepMind
Gemma 3 QAT 4B
🇺🇸 Google DeepMind
Demist-2
🇬🇧 Darktrace
Gemini 2.5 Flash (Apr 2025)
🇺🇸 Google DeepMind
UI-TARS-1.5
🇨🇳 ByteDance
Digest: Cyber AI Analyst
🇬🇧 Darktrace
MAI-DS-R1
🇺🇸 Microsoft
OpenAI o3
🇺🇸 OpenAI
OpenAI o4-mini
🇺🇸 OpenAI
Seedream 3.0
🇨🇳 ByteDance
Cohere Embed 4
🇨🇦 Cohere
Kolors 2.0 Image Generation
🇨🇳 Kuaishou Technology
TerraMind
🇺🇸 IBM
360Zhinao3-7B-O1.5
🇨🇳 360 Security Technology
GLM-4-32B-0414
🇨🇳 Zhipu AI
GLM-4-9B-0414
🇨🇳 Tsinghua University
GLM-Z1-Rumination-32B-0414
🇨🇳 Tsinghua University
Nemotron-H 47B
🇺🇸 NVIDIA
Nemotron-H 56B
🇺🇸 NVIDIA
Nemotron-H 8B
🇺🇸 NVIDIA
Pangu Ultra
🇨🇳 Huawei
AMIE (Articulate Medical Intelligence Explorer)
🇺🇸 Google DeepMind
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.