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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.
FlowER
🇺🇸 Massachusetts Institute of Technology (MIT)
Surya
🇺🇸 NASA
NVIDIA-Nemotron-Nano-12B-v2
🇺🇸 NVIDIA
NVIDIA-Nemotron-Nano-9B-v2
🇺🇸 NVIDIA
Nemotron Nano 9B v2
🇺🇸 NVIDIA
Qwen Image Edit
🇨🇳 Qwen
imagen 4 fast
imagen 4 fast
🇺🇸 Google DeepMind
Gemma 3 270M
🇺🇸 Google DeepMind
Ideogram Character
🇨🇦 Ideogram
Claude Opus 4.1
🇺🇸 Anthropic
GLM 4.5
🇨🇳 Zhipu AI
GLM-4.5-Air
🇨🇳 Zhipu AI
gpt-oss-120b
🇺🇸 OpenAI
gpt-oss-20b
🇺🇸 OpenAI
Hierarchical Reasoning Model (HPM)
🇸🇬 Sapient Intelligence
Qwen Image
🇨🇳 Qwen
Gemini 2.5 Deep Think
MindLink-32B
🇨🇳 Kunlun Inc.
MindLink-72B
🇨🇳 Kunlun Inc.
Tri-21B
🇰🇷 Trillion Labs
rStar-Math (Qwen2-Math-7B base)
🇨🇳 Qwen
rStar-Math (Qwen2-Math-7B base)
🇺🇸 Microsoft Research Asia
rStar-Math (Qwen2.5-Math-7B base)
🇨🇳 Qwen
rStar-Math (Qwen2.5-Math-7B base)
🇺🇸 Microsoft Research Asia
AlphaEarth Foundations (AEF)
🇺🇸 Google DeepMind
Llama Nemotron Super v1.5
🇺🇸 NVIDIA
Agentar-Fin-R1 32B
🇨🇳 Ant Group
Agentar-Fin-R1 8B
🇨🇳 Ant Group
Llama-3.3 Nemotron Super 49B v1.5
🇺🇸 NVIDIA
Qwen3-235B-A22B-Instruct (Jul 2025)
🇨🇳 Qwen
Qwen3-235B-A22B-Thinking (Jul 2025)
🇨🇳 Qwen
Seed Prover
🇨🇳 ByteDance
Qwen3-Coder-480B-A35B
🇨🇳 Qwen
gemini-2.5-flash-lite
🇺🇸 Google DeepMind
T-Pro 2.0
🇷🇺 T-Bank
ChatGPT agent
🇺🇸 OpenAI
OpenReasoning-Nemotron-32B
🇺🇸 NVIDIA
EXAONE 4.0 (1.2B)
🇰🇷 LG AI Research
EXAONE 4.0 (32B)
🇰🇷 LG AI Research
Gemini Embedding
🇺🇸 Google DeepMind
Kimi K2
🇨🇳 Moonshot
Grok 4 Heavy
🇺🇸 xAI
Grok 4
🇺🇸 xAI
dots.llm1
🇨🇳 Rednote
Jamba 1.7
🇮🇱 AI21 Labs
Devstral Medium 1.0
🇫🇷 Mistral AI
Devstral Small 1.1
🇫🇷 Mistral AI
ERNIE-4.5-0.3B
🇨🇳 Baidu
ERNIE-4.5-21B-A3B
🇨🇳 Baidu
ERNIE-4.5-300B-A47B
🇨🇳 Baidu
Hunyuan-A13B (open-source)
🇨🇳 Tencent
DiLoCoX (Qwen1.5-107B on WT-103)
🇺🇸 China Mobile
DiLoCoX (Qwen1.5-107B on WT-103)
🇨🇳 Qwen
AlphaGenome
🇺🇸 Google DeepMind
Gemini 2.5 Flash (Jun 2025)
🇺🇸 Google DeepMind
Mercury Coder Mini
🇺🇸 Inception Labs
Mercury Coder Small
🇺🇸 Inception Labs
gemini-2.5-flash
🇺🇸 Google DeepMind
gemini-2.5-pro
🇺🇸 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.