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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.
Hybrid H3-1.3B
🇺🇸 Stanford University
Hybrid H3-125M
🇺🇸 Stanford University
Hybrid H3-2.7B
🇺🇸 Stanford University
Hybrid H3-355M
🇺🇸 Stanford University
OPT-IML (175B)
🇺🇸 Meta AI
CaLM
🇺🇸 University of Oxford
text-embedding-ada-002
🇺🇸 OpenAI
TranceptEve
🇺🇸 University of Oxford
Stable Diffusion 2.1
🇺🇸 Stability AI
Vega v2
🇨🇳 Wuhan University
Transformer + GFM
🇺🇸 Nanjing University
ZymCTRL
🇬🇧 Basecamp Research
GPT-3.5 Turbo
🇺🇸 OpenAI
ALM 1.0
🇨🇳 Beijing Academy of Artificial Intelligence / BAAI
DiT-XL/2 + Discriminator Guidance
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
Discriminator Guidance
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
GPT-3.5
🇺🇸 OpenAI
CLUE
🇰🇷 Naver Clova
scFormer
🇺🇸 University of Toronto
AR-LDM
🇨🇳 Alibaba
Fusion in Encoder
🇰🇷 Samsung
Galactica
🇺🇸 Meta AI
Luminous Sparse
🇩🇪 Aleph Alpha
Innovative Drug-like Molecule Generation from
🇺🇸 University of Pittsburgh
BLOOMZ-176B
🇺🇸 Hugging Face
Mogrifier RLSTM (PTB)
🇬🇧 DeepMind
Mogrifier RLSTM (WT2)
🇬🇧 DeepMind
mT0-13B
🇺🇸 Hugging Face
eDiff-I
🇺🇸 NVIDIA
GearNet
🇺🇸 Mila - Quebec AI (originally Montreal Institute for Learning Algorithms)
Taiyi-Stable Diffusion
🇨🇳 IDEA CCNL
Transformer-XL + PowerSGD + L-Greco
🇺🇸 Institute of Science and Technology Austria (ISTA)
MIF-ST
🇺🇸 Microsoft Research
XY-LENTXL
🇺🇸 Microsoft
DiffSBDD (CrossDocked)
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Tk-Instruct
🇺🇸 University of Washington
Verbatim Memory Transformer (108M)
🇺🇸 Johns Hopkins University
Verbatim Memory Transformer (117M)
🇺🇸 Johns Hopkins University
Flan-PaLM 540B
Flan-T5 11B
LMSI-Palm
U-PaLM (540B)
GPT-2 + Progressive LRD
🇨🇳 Huawei
GenSLM
🇺🇸 University of Chicago
Instruct-GPT + Mind's Eye
Decaying Fast Weights Transformer (WT-103)
🇸🇬 Jenni
AlphaTensor
🇬🇧 DeepMind
DiffDock
🇺🇸 Massachusetts Institute of Technology (MIT)
AminoBert
🇺🇸 Harvard Medical School
NMST+GPT-2
🇺🇸 New York University (NYU)
GemNet-OC
🇺🇸 Technical University of Munich
Sparrow
🇬🇧 DeepMind
CPM-Ant
🇨🇳 Tsinghua University
CPAC
🇺🇸 Texas A&M
DistilProtBert
🇺🇸 Bar-Ilan University
NeMO Megatron GPT 20B
🇺🇸 NVIDIA
ProteinMPNN
🇺🇸 University of Washington
Deep-LDA
🇨🇳 Fudan University
PaLI
Stable Diffusion 1.1
🇩🇪 Ludwig Maximilian University of Munich
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.