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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.
QRNN
🇺🇸 Salesforce Research
T-DMCA
🇺🇸 Google Brain
ULM-FiT
🇺🇸 University of San Francisco
RNNLM + Dynamic KL Regularization
🇺🇸 Northwestern University
PixelSNAIL (CIFAR 10)
🇺🇸 University of California (UC) Berkeley
PixelSNAIL (ImageNet)
🇺🇸 University of California (UC) Berkeley
WGAN (Wasserstein GAN)
🇺🇸 Facebook AI Research
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)
🇺🇸 Carnegie Mellon University (CMU)
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)
🇺🇸 Carnegie Mellon University (CMU)
DCN+
🇺🇸 Salesforce Research
Fraternal dropout + AWD-LSTM 3-layer (PTB)
🇺🇸 University of Montreal / Université de Montréal
Fraternal dropout + AWD-LSTM 3-layer (WT2)
🇺🇸 Jagiellonian University
S-Norm
🇺🇸 University of Washington
PhraseCond
🇺🇸 Carnegie Mellon University (CMU)
AWD-LSTM + dynamic eval (PTB)
🇺🇸 University of Edinburgh
AWD-LSTM + dynamic eval (WT2)
🇺🇸 University of Edinburgh
LSTM + dynamic eval
🇺🇸 University of Edinburgh
ISS
🇺🇸 Duke University
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)
🇺🇸 Ben-Gurion University of the Negev
D-LSRC(100)+KN5 (PTB)
🇺🇸 Saarland University
AWD-LSTM - 3-layer LSTM (tied) + continuous cache pointer (PTB)
🇺🇸 Salesforce Research
AWD-LSTM - 3-layer LSTM (tied) + continuous cache pointer (WT2)
🇺🇸 Salesforce Research
ConvS2S (ensemble of 8 models)
🇺🇸 Meta AI
Transformer
🇺🇸 Google Research
PointNet++
🇺🇸 Stanford University
WGAN-GP
🇺🇸 Courant Institute of Mathematical Sciences
SEST
🇺🇸 Carnegie Mellon University (CMU)
VDCNN (on Amazon Review Full dataset)
🇺🇸 Facebook AI Research
MoE-Multi
🇺🇸 Jagiellonian University
PixelCNN++
🇺🇸 OpenAI
GCNN-14
🇺🇸 Facebook AI Research
LSTM (PTB)
🇺🇸 Facebook AI Research
LSTM (WT103)
🇺🇸 Facebook AI Research
LSTM (WT2)
🇺🇸 Facebook AI Research
Neural cache model (size=2000)
🇺🇸 Facebook AI Research
PointNet
🇺🇸 Stanford University
BIDAF
🇺🇸 University of Washington
NAS with base 8 and shared embeddings
🇺🇸 Google Brain
VD-LSTM+REAL Large
🇺🇸 Salesforce Research
VD-LSTM+REAL Medium
🇺🇸 Stanford University
VD-LSTM+REAL Small
🇺🇸 Stanford University
Differentiable neural computer
🇺🇸 Google DeepMind
GNMT
Pointer Sentinel-LSTM (WT2)
🇺🇸 MetaMind Inc
Pointer Sentinel-LSTM (medium)
🇺🇸 MetaMind Inc
Zoneout + Variational LSTM (PTB)
🇺🇸 MetaMind Inc
Zoneout + Variational LSTM (WT2)
🇺🇸 MetaMind Inc
Knowledge distillation student model
🇺🇸 Harvard University
Youtube recommendation model
Layer Normalization: Draw
🇺🇸 University of Toronto
Layer Normalization: Handwriting sequence generation
🇺🇸 University of Toronto
Layer Normalization: Skip Thoughts
🇺🇸 University of Toronto
Layer Normalization: The Attentive Reader
🇺🇸 University of Toronto
Character-enriched word2vec
🇺🇸 Facebook AI Research
fastText
🇺🇸 Facebook AI Research
node2vec
🇺🇸 Stanford University
Wide & Deep
DMN
🇺🇸 Salesforce
Part-of-sentence tagging model
🇺🇸 Carnegie Mellon University (CMU)
Named Entity Recognition model
🇺🇸 Carnegie Mellon University (CMU)
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.