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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.
Engine-Base (NE)
🇺🇸 Boston University
Engine-Medium(NE)
🇺🇸 Boston University
Gopher (280B)
🇬🇧 DeepMind
Gopher (7.1B)
🇬🇧 DeepMind
CTR-BERT
🇺🇸 Amazon
T-NLRv5 XXL
🇺🇸 Microsoft
GPT-2-Medium+Pixelfly
🇺🇸 Stanford University
GPT-2-Small+Pixelfly
🇺🇸 Stanford University
Quantized ADMM
🇺🇸 Chinese University of Hong Kong (CUHK)
Transformer LM + MinSen
🇺🇸 Chinese University of Hong Kong (CUHK)
Persia
🇨🇭 ETH Zurich
DeBERTaV3large
🇺🇸 Microsoft Research
ESM1v
🇺🇸 Facebook AI Research
A.X (Adot) 18B
🇰🇷 SK Telecom
EquiDock
🇺🇸 Massachusetts Institute of Technology (MIT)
KoGPT
🇰🇷 Kakao
GPT-2 (AMPS)
🇺🇸 University of California (UC) Berkeley
GPT2+CoreLM+Fine-Tuning
🇬🇷 Aristotle University of Thessaloniki
NCP-VAE (CIFAR 10)
🇺🇸 University of Illinois Urbana-Champaign (UIUC)
NCP-VAE (Celeba HQ)
🇺🇸 University of Illinois Urbana-Champaign (UIUC)
CodeT5-base
🇺🇸 Salesforce
Projected GAN
🇺🇸 Heidelberg University
S4
🇺🇸 Stanford University
Scatterbrain
🇺🇸 Stanford University
Eve
🇺🇸 Harvard Medical School
PMLM-large
🇺🇸 Microsoft Research Asia
WD+LR+M
🇨🇳 University of Cambridge
base LM+GNN (WT103)
🇨🇳 Shannon.AI
base LM+GNN+kNN
🇨🇳 Shannon.AI
GPT-2 (fine-tuned with HYDRA)
🇺🇸 University of California San Diego
MGK 4 heads (medium)
🇺🇸 FPT Software AI Center
MGK 8 heads (small)
🇺🇸 FPT Software AI Center
PAGnol-XL
🇫🇷 LightOn
KnGPT2
🇨🇳 Huawei Noah's Ark Lab
T0-XXL
🇺🇸 Hugging Face
TOME
🇺🇸 University of Southern California
Yuan 1.0
🇨🇳 Inspur
Megatron-Turing NLG 530B
🇺🇸 Microsoft
AlphaFold-Multimer
🇺🇸 Google DeepMind
Turing ULRv5
🇺🇸 Microsoft
LM-GVP
🇺🇸 Amazon Machine Learning Solutions Lab
PLATO-XL
🇨🇳 Baidu
DLRM-2022
MegaMolBART
🇺🇸 NVIDIA
HyperCLOVA 204B
🇰🇷 NAVER
HyperCLOVA 82B
🇰🇷 NAVER
NLM
🇺🇸 Carnegie Mellon University (CMU)
PermuteFormer
🇨🇳 Peking University
RNS-RNN
🇺🇸 University of Notre Dame
MEB
🇺🇸 Microsoft
FLAN 137B
🇺🇸 Google Research
PLUS-RNN
🇺🇸 Seoul National University
$\infty$-former (SM)
🇬🇧 Universidade de Lisboa (ULisboa)
HJRSS
🇺🇸 University of Washington
ALiBi (L=3072, Lvalid = 3072)
🇺🇸 University of Washington
ProteinLM
🇨🇳 Tsinghua University
XLMR-XXL
🇺🇸 Facebook AI Research
DNABERT
🇺🇸 Northeastern University
GPT-2 (1.5B, Curriculum Learning 45K)
🇺🇸 Microsoft
GPT-2 (117M, SLW 110K)
🇺🇸 Microsoft
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.