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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.
Gemma 3 QAT 27B
🇺🇸 Google DeepMind
Gemma 3 QAT 4B
🇺🇸 Google DeepMind
Gemini 2.5 Flash (Apr 2025)
🇺🇸 Google DeepMind
MAI-DS-R1
🇺🇸 Microsoft
OpenAI o3
🇺🇸 OpenAI
OpenAI o4-mini
🇺🇸 OpenAI
TerraMind
🇺🇸 IBM
Nemotron-H 47B
🇺🇸 NVIDIA
Nemotron-H 56B
🇺🇸 NVIDIA
Nemotron-H 8B
🇺🇸 NVIDIA
AMIE (Articulate Medical Intelligence Explorer)
🇺🇸 Google DeepMind
SHIFT-SUV
🇺🇸 Luminary Cloud
T5Gemma (Gemma 9B-9B)
🇺🇸 Google DeepMind
TxGemma 27B
🇺🇸 Google DeepMind
TxGemma 2B
🇺🇸 Google DeepMind
TxGemma 9B
🇺🇸 Google DeepMind
Llama-3.1 Nemotron Ultra 253B
🇺🇸 NVIDIA
FoundationStereo
🇺🇸 NVIDIA
Phi-4-mini-reasoning
🇺🇸 Microsoft
Amazon Nova Act
🇺🇸 Amazon
DeepHermes 3 - Mistral 24B
🇺🇸 Nous Research
4o Image Generation
🇺🇸 OpenAI
Gemini 2.5 Pro (Mar 2025)
🇺🇸 Google DeepMind
Sonar Reasoning Pro
🇺🇸 Perplexity
3DGUT
🇺🇸 NVIDIA
HiCEGNN
🇺🇸 University of Missouri
Llama Nemotron Nano 8B
🇺🇸 NVIDIA
Llama Nemotron Super 49B
🇺🇸 NVIDIA
Llama Nemotron Ultra 253B
🇺🇸 NVIDIA
Llama-3.1 Nemotron Nano 8B
🇺🇸 NVIDIA
Llama-3.3 Nemotron Super 49B v1
🇺🇸 NVIDIA
OLMo 2 32B
🇺🇸 Allen Institute for AI
Gemma 3 12B
🇺🇸 Google DeepMind
Gemma 3 1B
🇺🇸 Google DeepMind
Gemma 3 27B
🇺🇸 Google DeepMind
Gemma 3 4B
🇺🇸 Google DeepMind
Difix3D+
🇺🇸 NVIDIA
Phi-4 Mini
🇺🇸 Microsoft
GPT-4.5
🇺🇸 OpenAI
Mercury
🇺🇸 Inception Labs
Granite 3.2
🇺🇸 IBM
Granite 3.2 2B
🇺🇸 IBM
Granite 3.2 8B
🇺🇸 IBM
Granite Guardian 3.2
🇺🇸 IBM
gemini-2.0-flash-lite
🇺🇸 Google DeepMind
Claude 3.7 Sonnet
🇺🇸 Anthropic
Claude Sonnet 3.7
🇺🇸 Anthropic
Evo 2 40B
🇺🇸 Arc Institute
Evo 2 7B
🇺🇸 Arc Institute
Grok 3 Beta
🇺🇸 xAI
Grok-3 mini
🇺🇸 xAI
Brain2Qwerty
🇺🇸 Meta AI
R1 1776
🇺🇸 Perplexity
Grok 3
🇺🇸 xAI
BRIA 3.1
🇺🇸 BRIA AI
Deephermes 3 Llama 3 8B Preview
🇺🇸 Nous Research
Granite Vision 3.2 2B
🇺🇸 IBM
Sonar Deep Research
🇺🇸 Perplexity
Sonar
🇺🇸 Perplexity
Prithvi-EO-2.0 300M
🇺🇸 IBM Research
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.