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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.
GLM-5.2
🇨🇳 Zhipu AI
DiffusionGemma 26B-A4B
🇺🇸 Google DeepMind
NVIDIA Nemotron 3 Ultra
🇺🇸 NVIDIA
LFM2.5-8B-A1B
🇺🇸 Liquid AI
Domyn Small
🇮🇹 Domyn
FastwebMIIA 7B (2026)
🇮🇹 Fastweb
nesso 0.4B Agentic
🇮🇹 mii-llm
nesso 0.4B Instruct
🇮🇹 mii-llm
nesso 4B
🇮🇹 mii-llm
Velvet 25B
🇮🇹 Almawave
Qwen3-GNR-it 14B
🇮🇹 Fondazione Bruno Kessler
ANITA-NEXT 20B
🇮🇹 University of Bari
Cohere Command A Reasoning
🇨🇦 Cohere
Teuken 7B
🇩🇪 OpenGPT-X
EXAONE 4.0 (1.2B)
🇰🇷 LG AI Research
EXAONE 4.0 (32B)
🇰🇷 LG AI Research
CollabLLM
🇺🇸 Stanford University
Reason-ModernColBERT
🇫🇷 LightOn
SANA 1.5 4.8B
🇺🇸 NVIDIA
II-Medical-8B
🇬🇧 Intelligent Internet
Earth-2 (cBottle-SR)
🇺🇸 NVIDIA
Kevin-32B
🇺🇸 Cognition
Nemotron-H 47B
🇺🇸 NVIDIA
Nemotron-H 56B
🇺🇸 NVIDIA
Nemotron-H 8B
🇺🇸 NVIDIA
SHIFT-SUV
🇺🇸 Luminary Cloud
FoundationStereo
🇺🇸 NVIDIA
EXAONE Deep 2.4B
🇰🇷 LG AI Research
EXAONE Deep 32B
🇰🇷 LG AI Research
EXAONE Deep 7.8B
🇰🇷 LG AI Research
Cohere Command A
🇨🇦 Cohere
Difix3D+
🇺🇸 NVIDIA
BRIA 3.1
🇺🇸 BRIA AI
EngGPT2 16B-A3B
🇮🇹 Engineering Ingegneria Informatica
Zagreus 0.4B
🇮🇹 mii-llm
RMBG v2.0
🇺🇸 BRIA AI
EXAONE 3.5 2.4B
🇰🇷 LG AI Research
EXAONE 3.5 32B
🇰🇷 LG AI Research
EXAONE 3.5 7.8B
🇰🇷 LG AI Research
Gemma 2 9B Neogenesis ITA
🇮🇹 anakin87
BiRNA-BERT
🇺🇸 Bangladesh University of Engineering and Technology
LLaVA-CoT
🇨🇳 Peking University
Mistral Large 2.1
🇫🇷 Mistral AI
Athene-V2
🇺🇸 Nexusflow
OpenPhenom-S/16
🇺🇸 Recursion Pharmaceuticals
Minitron 4B
🇺🇸 NVIDIA
Minitron 8B
🇺🇸 NVIDIA
Depth Anything V2 Large
🇨🇳 Tik Tok
Ministral 8B
🇫🇷 Mistral AI
CHAI-1
🇺🇸 Chai discovery
SANA 1.6B
🇺🇸 NVIDIA
Yuel 2
🇺🇸 Pennsylvania State University
EnzymeFlow
🇺🇸 McGill University
Phi-3.5-mini ITA
🇮🇹 anakin87
ConoDL
🇨🇳 Chongqing University
IgGM
🇨🇳 Chinese Academy of Sciences
GeoSeqBuilder
🇨🇳 Peking University
Qwen2.5-3B
🇨🇳 Qwen
Mistral Small v24.09
🇫🇷 Mistral AI
Novae
🇫🇷 CentraleSupelec
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.