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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.
Transformer + Average Attention Network
🇺🇸 University of Electronic Science and Technology of China
Transformer ELMo
🇺🇸 Allen Institute for AI
StyleGAN
🇺🇸 NVIDIA
SPN (CelebA HQ)
🇺🇸 Google Brain
SPN (ImageNet 128)
🇺🇸 Google Brain
DMPFold
🇨🇳 University College London (UCL)
GPipe (Transformer)
Multi-cell LSTM
🇮🇳 University of Hyderabad
Discriminator-tuned LSTM
🇷🇺 Samsung R&D Institute Russia
Fine-tuned-AWD-LSTM-DOC (fin)
🇷🇺 Samsung R&D Institute Russia
Mesh-TensorFlow Transformer 2.9B (translation)
🇺🇸 Google Brain
Mesh-TensorFlow Transformer 4.9B (language)
🇺🇸 Google Brain
MemoReader
🇰🇷 Samsung
code2vec
🇺🇸 Technion - Israel Institute of Technology
TrellisNet
🇺🇸 Carnegie Mellon University (CMU)
TrellisNet-MoS (1.4x larger) PTB
🇺🇸 Carnegie Mellon University (CMU)
BERT-Large
DeepConPred2
🇨🇳 Tsinghua University
ADP-FAIRSEQ + NGRAMRES
🇨🇳 Nara Institute of Science and Technology
BigGAN-deep 512x512
🇬🇧 Heriot-Watt University
Transformer (Adaptive Input Embeddings) WT103
🇺🇸 Facebook AI Research
LSTM+NeuralCache
🇺🇸 KU Leuven
AWD-LSTM-MoS + dynamic evaluation (PTB, 2018)
🇨🇳 Peking University
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)
🇨🇳 Peking University
Talent Search and Recommendation Systems
Transformer + Simple Recurrent Unit
🇺🇸 ASAPP
NetSurfP-2.0
🇩🇰 Technical University of Denmark
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)
🇯🇵 NTT Communication Science Laboratories
(ensemble): AWD-LSTM-DOC (fin) × 5 (WT2)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM-DOC (fin) (23M)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM-DOC (fin) (37M)
🇯🇵 NTT Communication Science Laboratories
Big Transformer for Back-Translation
🇺🇸 Facebook AI Research
AWD-LSTM-MoS+PDR + dynamic evaluation (PTB)
🇺🇸 IBM
AWD-LSTM-MoS+PDR + dynamic evaluation (WT2)
🇺🇸 IBM
RGC+ASQ (PTB)
🇨🇳 Tsinghua University
Glow (Celeba HQ)
🇺🇸 OpenAI
RCAN
🇺🇸 Northeastern University
S + I-Attention (3)
🇷🇺 National Research University Higher School of Economics
DARTS
🇬🇧 DeepMind
DARTS (second order) (PTB)
🇺🇸 Carnegie Mellon University (CMU)
Relational Memory Core
🇬🇧 DeepMind
GPT-1
🇺🇸 OpenAI
2-layer skip-LSTM + dropout tuning (PTB)
🇬🇧 DeepMind
RHN(depth=40)
🇮🇱 Ben-Gurion University
RHN+HSG(depth=40)
🇮🇱 Ben-Gurion University
aLSTM(depth-2)+RecurrentPolicy (PTB)
🇬🇧 University of Manchester
aLSTM(depth-2)+RecurrentPolicy (WT2)
🇬🇧 University of Manchester
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Bernoulli) (WT2)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Bernoulli) - large(PTB)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Laplace) - medium (WT2)
🇺🇸 Columbia University
LSTM+Noise(Beta)
🇺🇸 Columbia University
DNCON2
🇺🇸 University of Missouri
TF-LM-discourse LSTM (PTB)
🇺🇸 ESAT - PSI
TF-LM-discourse LSTM (WT2)
🇺🇸 ESAT - PSI
RNNLM + Dynamic KL Regularization (WT2)
🇺🇸 Northwestern University
RNMT+
🇺🇸 Google AI
LSTM (Hebbian, Cache, MbPA)
🇬🇧 DeepMind
4 layer QRNN (h=2500)
🇺🇸 Salesforce Research
Chinese - English translation
🇺🇸 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.