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Models Released in 2016
Browse every generative AI model released in 2016 — 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 2015 and models from 2017.
fastText
🇺🇸 Facebook AI Research
node2vec
🇺🇸 Stanford University
CCL
🇨🇳 SenseTime
Wide & Deep
R-FCN
🇨🇳 Tsinghua University
DMN
🇺🇸 Salesforce
Segmental RNN
🇺🇸 University of Edinburgh
CMS-RCNN
IEEE
PixelCNN
🇺🇸 Google DeepMind
Part-of-sentence tagging model
🇺🇸 Carnegie Mellon University (CMU)
LRR-4X
🇺🇸 UC Irvine
Dueling DQN
🇺🇸 Google DeepMind
Symmetric Residual Encoder-Decoder Net
🇺🇸 Nanjing University
Binarized Neural Network (MNIST)
🇺🇸 Technion - Israel Institute of Technology
Template Adaptation
🇺🇸 University of Oxford
Named Entity Recognition model
🇺🇸 Carnegie Mellon University (CMU)
Double DQN
🇺🇸 Google DeepMind
Order-Embeddings of Images and Language
🇺🇸 University of Toronto
10 LSTMS + KN-5 (OPTIMAL WEIGHTS)
🇺🇸 Google Brain
BIG LSTM+CNN INPUTS
🇺🇸 Google Brain
A3C FF hs
Convolutional Pose Machines
🇺🇸 Carnegie Mellon University (CMU)
AlphaGo Lee
🇬🇧 DeepMind
2016 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 2016 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 2015 or AI models from 2017 to compare how capabilities, context windows and open-weights availability evolved year over year.