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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.
SemVec
🇺🇸 Microsoft Research
DistBelief NNLM
Bayesian automated hyperparameter tuning
🇺🇸 University of Toronto
RNN
🇺🇸 Microsoft Research
RNN+LDA
🇺🇸 Microsoft Research
RNN+LSA+KN5+cache (model combination w/ linear extrapolation)
🇺🇸 Microsoft Research
LSTM LM
🇩🇪 RWTH Aachen University
Context-dependent RNN
🇺🇸 Microsoft Research
Ngram corpus
LBL
🇨🇳 University College London (UCL)
HOGWILD!
🇺🇸 University of Wisconsin Madison
Adaptive Subgrad
🇺🇸 Technion - Israel Institute of Technology
Recursive sentiment autoencoder
🇺🇸 Stanford University
Cross-Lingual POS Tagger
🇺🇸 Carnegie Mellon University (CMU)
Vector Space Model
🇺🇸 Stanford University
RNN LM
🇺🇸 Johns Hopkins University
SimuParallelSGD
🇺🇸 Yahoo Research
Stacked Denoising Autoencoders
🇺🇸 University of Montreal / Université de Montréal
3D city reconstruction
🇺🇸 University of Washington
Pragmatic Theory solution (Netflix 2009)
🇨🇦 Pragmatic Theory Inc.
Conditional Maximum Entropy Model (Gigaworld)
GPU DBNs
🇺🇸 Stanford University
Deep Boltzmann Machines
🇺🇸 University of Toronto
ADAPTIVE NLPM
🇺🇸 University of Toronto
HLBL
🇺🇸 University of Toronto
Boss (DARPA Urban Challenge)
🇺🇸 Carnegie Mellon University (CMU)
Deep Multitask NLP Network
🇺🇸 NEC Laboratories
Denoising Autoencoders
🇺🇸 University of Montreal / Université de Montréal
Semi-Supervised Embedding for DL
Enhanced Neighborhood-Based Filtering
🇺🇸 AT&T
KN-LM
SB-LM
Empirical evaluation of deep architectures
🇺🇸 University of Montreal / Université de Montréal
Greedy layer-wise DNN training
🇺🇸 University of Montreal / Université de Montréal
RL for helicopter flight
🇺🇸 University of California (UC) Berkeley
Stanley (DARPA Grand Challenge 2)
🇺🇸 Stanford University
RankNet
🇺🇸 Microsoft Research
Hierarchical LM
Unknown
Sandstorm (DARPA Grand Challenge I)
🇺🇸 Carnegie Mellon University (CMU)
RankBoost (EachMovie)
🇺🇸 Columbia University
RankBoost (meta-search)
🇺🇸 Columbia University
NPLM (AP News)
🇺🇸 University of Montreal / Université de Montréal
NPLM (Brown)
🇺🇸 University of Montreal / Université de Montréal
LDA
🇺🇸 Stanford University
Web mining + Decision tree recommender
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
NEAT
🇺🇸 UT Austin
Tagging via Viterbi Decoding
🇺🇸 AT&T
Gradient Boosting Machine
🇺🇸 Stanford University
Immediate trihead
🇺🇸 Brown University
Neural LM
🇺🇸 University of Montreal / Université de Montréal
SVD in recommender systems
🇺🇸 University of Minnesota
RECONTRA-categorized
Unknown
RECONTRA-uncategorized
Unknown
Learning to Order Things
🇺🇸 AT&T
LSTM
🇺🇸 Technical University of Munich
n-gram LM
🇨🇳 University of Cambridge
Multi-cause Binary Clustering
🇺🇸 Xerox
Predictive Coding NN
🇺🇸 Technical University of Munich
Futures trading net
Unknown
Cancer drug mechanism prediction
🇺🇸 National Cancer Institute
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.