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Models Released in 2019
Browse every generative AI model released in 2019 — large language models, image generators, code models and more — ordered by release date. Compare their specs and rankings on our benchmarks page, or put any two side by side with compare. See also models from 2018 and models from 2020.
Big Transfer (BiT-M)
🇺🇸 Google Brain
DD-PPO
🇺🇸 Georgia Institute of Technology
SeqVec
🇺🇸 Technical University of Munich
OpenAI Five
🇺🇸 OpenAI
OpenAI Five Rerun
🇺🇸 OpenAI
MMLSTM (PTB)
🇨🇳 Beijing University of Posts and Telecommunications
MMLSTM (WT-103)
🇨🇳 Beijing University of Posts and Telecommunications
MMLSTM (WT-2)
🇨🇳 Beijing University of Posts and Telecommunications
StarGAN v2
🇰🇷 NAVER
StyleGAN2
🇺🇸 NVIDIA
bRSM + cache
🇺🇸 Numenta
AWD-LSTM + DeFINE
🇺🇸 University of Washington
Adaptive LSTM + DeFINE
🇺🇸 University of Washington
Transformer-XL DeFINE (107M)
🇺🇸 University of Washington
Transformer-XL DeFINE (141M)
🇺🇸 University of Washington
Photo-Geometric Autoencoder
🇺🇸 University of Oxford
FastSpeech
🇺🇸 Zhejiang University (ZJU)
MuZero
🇬🇧 DeepMind
Transformer - LibriVox + Decoding/Rescoring
Long-range sequence Compressive Transformers
🇬🇧 DeepMind
Noisy Student (L2)
🇺🇸 Carnegie Mellon University (CMU)
CamemBERT
Sandwich Transformer
🇺🇸 Allen Institute for AI
Self-Attention and Convolutional Layers
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
XLM-RoBERTa
🇺🇸 Facebook AI
Base LM + kNN LM + Continuous Cache
🇺🇸 Stanford University
AlphaStar
🇬🇧 DeepMind
BART-large
🇺🇸 Facebook AI
T5-11B
T5-3B
LSTM(large)+Sememe+cell
🇨🇳 Tsinghua University
LSTM(medium)+Sememe+cell (WT2)
🇨🇳 Tsinghua University
RMSNorm (Transformer-base)
🇺🇸 University of Edinburgh
Rubik's cube ADR robot
🇺🇸 OpenAI
M4-50B
AlphaX-1
🇺🇸 Facebook AI Research
DistilBERT
🇺🇸 Hugging Face
ALBERT
🇺🇸 Toyota Technological Institute at Chicago
Adaptive Inputs + LayerDrop
🇺🇸 Facebook AI Research
Alleviated TOI 10 (PTB)
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Alleviated TOI 10 (WT103)
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Alleviated TOI 10 (WT2)
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Hide and Seek
🇺🇸 OpenAI
Megatron-BERT
🇺🇸 NVIDIA
Megatron-LM (1.2B)
🇺🇸 NVIDIA
Megatron-LM (2.5B)
🇺🇸 NVIDIA
Megatron-LM (355M)
🇺🇸 NVIDIA
Megatron-LM (8.3B)
🇺🇸 NVIDIA
Xiaoice
🇺🇸 Microsoft Research Asia
ResNet-152 + ObjectNet
🇺🇸 Massachusetts Institute of Technology (MIT)
Mogrifier (d2, MC) + dynamic eval
🇬🇧 DeepMind
Mogrifier (d2, MoS2, MC) + dynamic eval
🇬🇧 DeepMind
UDSMProt
🇩🇪 Fraunhofer Heinrich Hertz Institute
DEQ-Transformer (Medium, Adaptive Embedding)
🇺🇸 Carnegie Mellon University (CMU)
DEQ-TrellisNet (PTB)
🇺🇸 Carnegie Mellon University (CMU)
DEQ-TrellisNet (WT-103)
🇺🇸 Carnegie Mellon University (CMU)
AWD-LSTM+Behaviorial-Gating
🇺🇸 University of Southern California
LSTM-Medium+Behaviorial-Gating (PTB)
🇺🇸 University of Southern California
trRosetta
🇺🇸 Nankai University
TripletRes
🇺🇸 University of Michigan
2019 was another fast-moving year for generative AI. The models listed above span large language models (LLMs), image and video generators, code models and more, each with its own parameter count, context window, licensing and availability. To understand how the models of 2019 actually perform, compare their scores on our benchmarks page, and use compare to evaluate any two releases side by side.
Tracking AI by release year makes it easy to see how the frontier moves. Browse AI models from 2018 or AI models from 2020 to compare how capabilities, context windows and open-weights availability evolved year over year.