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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.
Diffusion-GAN
🇺🇸 UT Austin
GPT-2 Medium (FlashAttention)
🇺🇸 Stanford University
Tranception
🇺🇸 University of Oxford
TRIMELMext (247M)
🇺🇸 Princeton University
TRIMELMext (7M)
🇺🇸 Princeton University
TRIMELMlong (150M)
🇺🇸 Princeton University
Imagen
🇺🇸 Google Brain
LSTM+GraB
🇺🇸 Cornell University
SimCSE
🇺🇸 Princeton University
UL2
🇺🇸 Google Research
DeBERTaV3large + KEAR
🇺🇸 Microsoft
OPT-175B
🇺🇸 Meta AI
GraphBP
🇺🇸 Texas A&M
Sparse all-MLP
🇺🇸 Meta AI
XMC-GAN
🇺🇸 Google Research
Stable Diffusion (LDM-KL-8-G)
🇺🇸 Runway
DALL·E 2
🇺🇸 OpenAI
PaLM (540B)
🇺🇸 Google Research
Monarch-GPT-2-Medium
🇺🇸 Stanford University
Monarch-GPT-2-Small
🇺🇸 Stanford University
NoPos
🇺🇸 Tel Aviv University
GraSR
🇺🇸 Shanghai Jiao Tong University
Make-A-Scene
🇺🇸 Meta AI
MemSizer (language modeling)
🇺🇸 Meta AI
Segatron -XL base, M=150 + HCP
🇺🇸 Microsoft Research
Segatron-XL large, M=384 + HCP
🇺🇸 Microsoft Research
Transformer Large + HCP
🇺🇸 University of Waterloo
GPT3-6.7B + muP
🇺🇸 Microsoft
MegaSyn
🇺🇸 Collaborations Pharmaceuticals
Statement Curriculum Learning
🇺🇸 OpenAI
DeepNet
🇺🇸 Microsoft Research
PolyCoder
🇺🇸 Carnegie Mellon University (CMU)
FourCastNet
🇺🇸 NVIDIA
ST-MoE
Midjourney V1
🇺🇸 Midjourney
LaMDA
ProteinBERT
🇺🇸 Hebrew University of Jerusalem
GPT-NeoX-20B
🇺🇸 EleutherAI
MaskGIT (ImageNet)
🇺🇸 Google Research
InstructGPT 1.3B
🇺🇸 OpenAI
InstructGPT 175B
🇺🇸 OpenAI
InstructGPT 350M
🇺🇸 OpenAI
InstructGPT 6B
🇺🇸 OpenAI
Primer (GPT-3 XL-like 1.9B)
🇺🇸 Google Brain
OntoProtein
🇺🇸 Zhejiang University (ZJU)
AbLang (heavy sequences)
🇺🇸 University of Oxford
data2vec (language)
🇺🇸 Meta AI
Vespa
🇺🇸 Technical University of Munich
Fairseq-dense 13B
🇺🇸 Meta AI
GLIDE
🇺🇸 OpenAI
LDM-1.45B
🇺🇸 Heidelberg University
MoE-1.1T
🇺🇸 Meta AI
XGLM-7.5B
🇺🇸 Meta AI
Contriever
🇺🇸 Meta AI
HSO
🇺🇸 Toyota Technological Institute at Chicago
LongT5
🇺🇸 Google Research
GLaM
Engine-Base (NE)
🇺🇸 Boston University
Engine-Medium(NE)
🇺🇸 Boston University
CTR-BERT
🇺🇸 Amazon
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.