{
  "metadata": {
    "title": "AI Training Compute — Epoch AI",
    "description": "Training compute (FLOP) for notable AI models. Frontier envelope shows the most compute-intensive model at each point in time.",
    "source": "Epoch AI, \"Data on AI Models\". https://epoch.ai/data/ai-models",
    "license": "CC-BY-4.0",
    "citation": "Epoch AI, \"Data on AI Models\". Published online at epoch.ai. Retrieved from https://epoch.ai/data/ai-models [online resource].",
    "generated": "2026-04-16",
    "totalModels": 521,
    "frontierPoints": 41
  },
  "allPoints": [
    {
      "year": 1950.5,
      "logFlop": 1.6,
      "label": "Theseus",
      "organization": "Bell Laboratories",
      "domain": "Robotics"
    },
    {
      "year": 1957.0,
      "logFlop": 5.84,
      "label": "Perceptron Mark I",
      "organization": "Cornell Aeronautical Laboratory,Cornell University",
      "domain": "Other"
    },
    {
      "year": 1959.09,
      "logFlop": 8.78,
      "label": "Pandemonium (morse)",
      "organization": "Massachusetts Institute of Technology (MIT)",
      "domain": "Language"
    },
    {
      "year": 1959.5,
      "logFlop": 8.63,
      "label": "Samuel Neural Checkers",
      "organization": "IBM",
      "domain": "Games"
    },
    {
      "year": 1960.24,
      "logFlop": 8.86,
      "label": "Perceptron (1960)",
      "organization": "Cornell Aeronautical Laboratory",
      "domain": "Vision"
    },
    {
      "year": 1960.49,
      "logFlop": 3.82,
      "label": "ADALINE",
      "organization": "Stanford University",
      "domain": "Vision"
    },
    {
      "year": 1962.42,
      "logFlop": 6.19,
      "label": "Linear Decision Functions",
      "organization": "Bell Laboratories",
      "domain": "Mathematics"
    },
    {
      "year": 1963.0,
      "logFlop": 7.35,
      "label": "Print Recognition Logic",
      "organization": "IBM",
      "domain": "Vision"
    },
    {
      "year": 1965.75,
      "logFlop": 6.03,
      "label": "Heuristic Reinforcement Learning",
      "organization": "Purdue University",
      "domain": "Robotics"
    },
    {
      "year": 1966.84,
      "logFlop": 8.02,
      "label": "LTE speaker verification system",
      "organization": "IBM",
      "domain": "Speech"
    },
    {
      "year": 1975.67,
      "logFlop": 6.71,
      "label": "Cognitron",
      "organization": "Biological Cybernetics",
      "domain": "Other"
    },
    {
      "year": 1980.25,
      "logFlop": 8.44,
      "label": "Neocognitron",
      "organization": "NHK Broadcasting Science Research Laboratories",
      "domain": "Vision"
    },
    {
      "year": 1983.67,
      "logFlop": 8.51,
      "label": "ASE+ACE",
      "organization": "University of Massachusetts Amherst",
      "domain": "Robotics"
    },
    {
      "year": 1986.62,
      "logFlop": 8.59,
      "label": "Distributed representation NN",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Other"
    },
    {
      "year": 1986.75,
      "logFlop": 8.83,
      "label": "MLP with back-propagation",
      "organization": "University of California San Diego,Carnegie Mellon University (CMU)",
      "domain": "Mathematics"
    },
    {
      "year": 1987.43,
      "logFlop": 10.44,
      "label": "NetTalk (dictionary)",
      "organization": "Princeton University",
      "domain": "Speech"
    },
    {
      "year": 1987.43,
      "logFlop": 10.45,
      "label": "NetTalk (transcription)",
      "organization": "Princeton University",
      "domain": "Speech"
    },
    {
      "year": 1987.45,
      "logFlop": 10.26,
      "label": "Translation-invariant MLP",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": ""
    },
    {
      "year": 1988.58,
      "logFlop": 8.47,
      "label": "MLN-ASR",
      "organization": "McGill University",
      "domain": "Speech"
    },
    {
      "year": 1989.46,
      "logFlop": 10.43,
      "label": "Invariant image recognition",
      "organization": "Complutense University of Madrid",
      "domain": "Vision"
    },
    {
      "year": 1989.91,
      "logFlop": 9.87,
      "label": "Speaker-independent vowel classification",
      "organization": "University of Washington",
      "domain": "Speech"
    },
    {
      "year": 1989.91,
      "logFlop": 11.26,
      "label": "Handwritten digit recognition network",
      "organization": "AT&T",
      "domain": "Vision"
    },
    {
      "year": 1989.92,
      "logFlop": 12.18,
      "label": "Zip CNN",
      "organization": "AT&T,Bell Laboratories",
      "domain": "Vision"
    },
    {
      "year": 1990.42,
      "logFlop": 10.55,
      "label": "NETtalk reimplementation",
      "organization": "Oregon State University",
      "domain": "Speech"
    },
    {
      "year": 1990.46,
      "logFlop": 9.49,
      "label": "Bankruptcy-NN",
      "organization": "",
      "domain": "Other"
    },
    {
      "year": 1990.75,
      "logFlop": 10.9,
      "label": "SexNet compression",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1991.92,
      "logFlop": 10.88,
      "label": "Weight Decay",
      "organization": "",
      "domain": "Speech"
    },
    {
      "year": 1992.33,
      "logFlop": 13.26,
      "label": "TD-Gammon",
      "organization": "IBM",
      "domain": "Games"
    },
    {
      "year": 1992.79,
      "logFlop": 7.73,
      "label": "Cancer drug mechanism prediction",
      "organization": "National Cancer Institute",
      "domain": "Medicine"
    },
    {
      "year": 1993.58,
      "logFlop": 13.11,
      "label": "Siamese-TDNN",
      "organization": "Bell Laboratories",
      "domain": "Vision"
    },
    {
      "year": 1993.91,
      "logFlop": 10.24,
      "label": "ANN Eye Tracker",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1994.02,
      "logFlop": 9.66,
      "label": "Ceramic-MLP",
      "organization": "Sapienza Università di Roma",
      "domain": "Materials science"
    },
    {
      "year": 1994.92,
      "logFlop": 7.91,
      "label": "JPMAX",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1994.92,
      "logFlop": 11.66,
      "label": "Mixture of linear models",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1994.92,
      "logFlop": 11.93,
      "label": "NeuroChess",
      "organization": "",
      "domain": "Games"
    },
    {
      "year": 1994.92,
      "logFlop": 13.27,
      "label": "Predictive Coding NN",
      "organization": "Technical University of Munich",
      "domain": "Language"
    },
    {
      "year": 1995.91,
      "logFlop": 11.29,
      "label": "LISSOM",
      "organization": "University of Texas at Austin",
      "domain": "Vision"
    },
    {
      "year": 1996.42,
      "logFlop": 11.95,
      "label": "MUSIC perceptron",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1996.46,
      "logFlop": 10.41,
      "label": "System 11",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Vision"
    },
    {
      "year": 1997.08,
      "logFlop": 10.5,
      "label": "SOM-CNN",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 1997.87,
      "logFlop": 13.5,
      "label": "LSTM",
      "organization": "Technical University of Munich",
      "domain": "Language"
    },
    {
      "year": 1998.84,
      "logFlop": 12.45,
      "label": "LeNet-5",
      "organization": "AT&T",
      "domain": "Vision"
    },
    {
      "year": 1999.42,
      "logFlop": 12.59,
      "label": "RECONTRA-uncategorized",
      "organization": "",
      "domain": "Language"
    },
    {
      "year": 1999.42,
      "logFlop": 12.9,
      "label": "RECONTRA-categorized",
      "organization": "",
      "domain": "Language"
    },
    {
      "year": 2000.91,
      "logFlop": 13.71,
      "label": "PoE MNIST",
      "organization": "University College London (UCL)",
      "domain": "Vision"
    },
    {
      "year": 2000.91,
      "logFlop": 15.8,
      "label": "Neural LM",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2001.94,
      "logFlop": 13.8,
      "label": "Decision tree (classification)",
      "organization": "Mitsubishi Electric Research Labs,Compaq CRL",
      "domain": "Vision"
    },
    {
      "year": 2003.2,
      "logFlop": 14.12,
      "label": "NPLM (Brown)",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2003.2,
      "logFlop": 15.22,
      "label": "NPLM (AP News)",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2004.49,
      "logFlop": 11.99,
      "label": "Invariant CNN",
      "organization": "New York University (NYU)",
      "domain": "Vision"
    },
    {
      "year": 2004.92,
      "logFlop": 15.44,
      "label": "LMICA",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 2005.02,
      "logFlop": 14.06,
      "label": "Hierarchical LM",
      "organization": "",
      "domain": "Language"
    },
    {
      "year": 2005.6,
      "logFlop": 12.54,
      "label": "RankNet",
      "organization": "Microsoft Research,Microsoft",
      "domain": "Search"
    },
    {
      "year": 2006.46,
      "logFlop": 14.87,
      "label": "SVM-CNN",
      "organization": "New York University (NYU)",
      "domain": "Vision"
    },
    {
      "year": 2007.47,
      "logFlop": 17.89,
      "label": "KN-LM",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2007.47,
      "logFlop": 18.16,
      "label": "SB-LM",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2008.94,
      "logFlop": 9.21,
      "label": "GNN",
      "organization": "University of Siena",
      "domain": "Other"
    },
    {
      "year": 2009.45,
      "logFlop": 15.0,
      "label": "GPU DBNs",
      "organization": "Stanford University",
      "domain": "Other"
    },
    {
      "year": 2009.67,
      "logFlop": 13.32,
      "label": "Two Stage Feature Extraction (MNIST)",
      "organization": "New York University (NYU)",
      "domain": "Vision"
    },
    {
      "year": 2009.89,
      "logFlop": 15.39,
      "label": "LCNP NORB",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 2009.89,
      "logFlop": 15.52,
      "label": "LCNP LabelMe",
      "organization": "University of Bonn",
      "domain": "Vision"
    },
    {
      "year": 2009.89,
      "logFlop": 15.62,
      "label": "LCNP MNIST",
      "organization": "",
      "domain": "Vision"
    },
    {
      "year": 2010.36,
      "logFlop": 14.54,
      "label": "Feedforward NN",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Vision"
    },
    {
      "year": 2010.45,
      "logFlop": 15.03,
      "label": "iCCCP",
      "organization": "Massachusetts Institute of Technology (MIT)",
      "domain": "Vision"
    },
    {
      "year": 2010.71,
      "logFlop": 15.09,
      "label": "Pooling CNN (Caltech 101)",
      "organization": "University of Bonn",
      "domain": "Vision"
    },
    {
      "year": 2010.71,
      "logFlop": 15.16,
      "label": "Pooling CNN (NORB)",
      "organization": "University of Bonn",
      "domain": "Vision"
    },
    {
      "year": 2010.74,
      "logFlop": 16.73,
      "label": "RNN LM",
      "organization": "Johns Hopkins University",
      "domain": "Language"
    },
    {
      "year": 2011.32,
      "logFlop": 16.57,
      "label": "Deep Autoencoders",
      "organization": "University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2011.54,
      "logFlop": 16.41,
      "label": "High Performance CNN (NORB)",
      "organization": "IDSIA,SUPSI",
      "domain": "Vision"
    },
    {
      "year": 2011.72,
      "logFlop": 16.41,
      "label": "CNN Committee (NIST)",
      "organization": "IDSIA",
      "domain": "Vision"
    },
    {
      "year": 2011.72,
      "logFlop": 16.72,
      "label": "CNN Committee (MNIST)",
      "organization": "IDSIA",
      "domain": "Vision"
    },
    {
      "year": 2011.76,
      "logFlop": 15.0,
      "label": "CNN committee (traffic sign)",
      "organization": "IDSIA",
      "domain": "Vision"
    },
    {
      "year": 2012.42,
      "logFlop": 15.63,
      "label": "Dropout (CIFAR)",
      "organization": "University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2012.42,
      "logFlop": 15.78,
      "label": "Dropout (MNIST)",
      "organization": "University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2012.42,
      "logFlop": 17.44,
      "label": "Dropout (ImageNet)",
      "organization": "University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2012.53,
      "logFlop": 17.78,
      "label": "Unsupervised High-level Feature Learner",
      "organization": "Google",
      "domain": "Vision"
    },
    {
      "year": 2012.69,
      "logFlop": 16.22,
      "label": "LSTM LM",
      "organization": "RWTH Aachen University",
      "domain": "Language"
    },
    {
      "year": 2012.74,
      "logFlop": 17.67,
      "label": "AlexNet",
      "organization": "University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2012.92,
      "logFlop": 17.49,
      "label": "DistBelief Speech",
      "organization": "Google",
      "domain": "Speech"
    },
    {
      "year": 2012.92,
      "logFlop": 17.68,
      "label": "DNN EM segmentation",
      "organization": "IDSIA,SUPSI",
      "domain": "Vision"
    },
    {
      "year": 2013.04,
      "logFlop": 18.42,
      "label": "DistBelief NNLM",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2013.4,
      "logFlop": 17.11,
      "label": "ReLU-Speech",
      "organization": "Google,University of Toronto,New York University (NYU)",
      "domain": "Speech"
    },
    {
      "year": 2013.58,
      "logFlop": 17.38,
      "label": "Hierarchical Scene Labeling (Stanford Background)",
      "organization": "New York University (NYU)",
      "domain": "Vision"
    },
    {
      "year": 2013.75,
      "logFlop": 15.97,
      "label": "RCTM",
      "organization": "University of Oxford",
      "domain": "Language"
    },
    {
      "year": 2013.75,
      "logFlop": 16.15,
      "label": "RNTN",
      "organization": "Stanford University",
      "domain": "Language"
    },
    {
      "year": 2013.79,
      "logFlop": 16.59,
      "label": "Word2Vec (large)",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2013.87,
      "logFlop": 17.73,
      "label": "Visualizing CNNs",
      "organization": "New York University (NYU)",
      "domain": "Vision"
    },
    {
      "year": 2013.93,
      "logFlop": 18.13,
      "label": "TransE",
      "organization": "Universite de Technologie de Compiègne – CNRS,Google",
      "domain": "Language"
    },
    {
      "year": 2013.97,
      "logFlop": 14.68,
      "label": "Image generation",
      "organization": "University of Amsterdam",
      "domain": "Vision"
    },
    {
      "year": 2013.97,
      "logFlop": 15.45,
      "label": "DQN",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2014.44,
      "logFlop": 17.71,
      "label": "GANs",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Image generation"
    },
    {
      "year": 2014.46,
      "logFlop": 18.53,
      "label": "SPPNet",
      "organization": "Microsoft,Xi’an Jiaotong University,University of Science and Technology of China (USTC)",
      "domain": "Vision"
    },
    {
      "year": 2014.5,
      "logFlop": 16.84,
      "label": "SmooCT",
      "organization": "University College London (UCL)",
      "domain": "Games"
    },
    {
      "year": 2014.54,
      "logFlop": 13.88,
      "label": "ACF-WIDER",
      "organization": "Chinese Academy of Sciences",
      "domain": "Vision"
    },
    {
      "year": 2014.67,
      "logFlop": 18.19,
      "label": "RNNsearch-50*",
      "organization": "Jacobs University Bremen,University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2014.68,
      "logFlop": 19.04,
      "label": "VGG19",
      "organization": "University of Oxford",
      "domain": "Vision"
    },
    {
      "year": 2014.68,
      "logFlop": 19.09,
      "label": "VGG16",
      "organization": "University of Oxford",
      "domain": "Vision"
    },
    {
      "year": 2014.69,
      "logFlop": 19.75,
      "label": "Seq2Seq LSTM",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2014.7,
      "logFlop": 16.64,
      "label": "SPN-4+KN5",
      "organization": "Singapore University of Technology & Design,DSO National Laboratories",
      "domain": "Language"
    },
    {
      "year": 2014.71,
      "logFlop": 18.18,
      "label": "GoogLeNet / InceptionV1",
      "organization": "Google,University of Michigan,University of North Carolina",
      "domain": "Vision"
    },
    {
      "year": 2014.91,
      "logFlop": 16.04,
      "label": "TA-CNN",
      "organization": "Chinese University of Hong Kong (CUHK)",
      "domain": "Vision"
    },
    {
      "year": 2014.92,
      "logFlop": 20.47,
      "label": "SNM-skip",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2014.96,
      "logFlop": 17.0,
      "label": "Fractional Max-Pooling",
      "organization": "University of Warwick",
      "domain": "Vision"
    },
    {
      "year": 2014.98,
      "logFlop": 14.8,
      "label": "ADAM (CIFAR-10)",
      "organization": "University of Amsterdam,OpenAI,University of Toronto",
      "domain": "Vision"
    },
    {
      "year": 2015.1,
      "logFlop": 19.38,
      "label": "MSRA (C, PReLU)",
      "organization": "Microsoft Research",
      "domain": "Vision"
    },
    {
      "year": 2015.21,
      "logFlop": 16.53,
      "label": "genCNN + dyn eval",
      "organization": "Chinese Academy of Sciences,Huawei Noah's Ark Lab,Dublin City University",
      "domain": "Language"
    },
    {
      "year": 2015.26,
      "logFlop": 17.29,
      "label": "TC-DNN-BLSTM-DNN",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Speech"
    },
    {
      "year": 2015.38,
      "logFlop": 16.71,
      "label": "U-Net",
      "organization": "University of Freiburg",
      "domain": "Vision"
    },
    {
      "year": 2015.6,
      "logFlop": 17.68,
      "label": "DCNN",
      "organization": "University of Maryland,Rutgers University",
      "domain": "Vision"
    },
    {
      "year": 2015.75,
      "logFlop": 20.58,
      "label": "AlphaGo Fan",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2015.82,
      "logFlop": 19.09,
      "label": "SAF R-CNN",
      "organization": "Beijing Institute of Technology,Sun Yat-sen University,Panasonic R&D,National University of Singapore",
      "domain": "Vision"
    },
    {
      "year": 2015.92,
      "logFlop": 20.0,
      "label": "Inception v3",
      "organization": "Google,University College London (UCL)",
      "domain": "Vision"
    },
    {
      "year": 2015.94,
      "logFlop": 18.85,
      "label": "ResNet-101 (ImageNet)",
      "organization": "Microsoft",
      "domain": "Vision"
    },
    {
      "year": 2015.94,
      "logFlop": 19.02,
      "label": "ResNet-152 (ImageNet)",
      "organization": "Microsoft",
      "domain": "Vision"
    },
    {
      "year": 2015.96,
      "logFlop": 15.77,
      "label": "Variational (untied weights, MC) LSTM (Large)",
      "organization": "University of Cambridge",
      "domain": "Language"
    },
    {
      "year": 2016.07,
      "logFlop": 21.28,
      "label": "AlphaGo Lee",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2016.17,
      "logFlop": 16.99,
      "label": "Named Entity Recognition model",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Language"
    },
    {
      "year": 2016.47,
      "logFlop": 17.86,
      "label": "R-FCN",
      "organization": "Tsinghua University,Microsoft Research",
      "domain": "Vision"
    },
    {
      "year": 2016.71,
      "logFlop": 19.47,
      "label": "ResNet-200",
      "organization": "Microsoft Research Asia",
      "domain": "Vision"
    },
    {
      "year": 2016.74,
      "logFlop": 15.87,
      "label": "Pointer Sentinel-LSTM (medium)",
      "organization": "MetaMind Inc,Salesforce",
      "domain": "Language"
    },
    {
      "year": 2016.74,
      "logFlop": 21.82,
      "label": "GNMT",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2016.77,
      "logFlop": 20.64,
      "label": "Xception",
      "organization": "Google",
      "domain": "Vision"
    },
    {
      "year": 2016.83,
      "logFlop": 16.26,
      "label": "SPIDER2",
      "organization": "Griffith University,University of Iowa,Dezhou University",
      "domain": "Biology"
    },
    {
      "year": 2016.84,
      "logFlop": 16.33,
      "label": "VD-LSTM+REAL Large",
      "organization": "Salesforce Research,Stanford University",
      "domain": "Language"
    },
    {
      "year": 2016.85,
      "logFlop": 16.02,
      "label": "NAS with base 8 and shared embeddings",
      "organization": "Google Brain",
      "domain": "Language"
    },
    {
      "year": 2016.85,
      "logFlop": 18.54,
      "label": "BIDAF",
      "organization": "University of Washington,Allen Institute for AI",
      "domain": "Language"
    },
    {
      "year": 2016.85,
      "logFlop": 21.34,
      "label": "NASv3 (CIFAR-10)",
      "organization": "Google Brain",
      "domain": "Vision"
    },
    {
      "year": 2016.88,
      "logFlop": 19.08,
      "label": "ResNeXt-101 (64×4d)",
      "organization": "University of California San Diego,Facebook",
      "domain": "Vision"
    },
    {
      "year": 2016.88,
      "logFlop": 19.81,
      "label": "PolyNet",
      "organization": "Chinese University of Hong Kong (CUHK)",
      "domain": "Vision"
    },
    {
      "year": 2016.95,
      "logFlop": 18.85,
      "label": "HR-ResNet101",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Vision"
    },
    {
      "year": 2016.98,
      "logFlop": 17.12,
      "label": "EnhanceNet",
      "organization": "Max Planck Institute for Intelligent Systems",
      "domain": "Vision"
    },
    {
      "year": 2017.02,
      "logFlop": 19.16,
      "label": "DeepStack",
      "organization": "University of Alberta,Charles University,Czech Technical University",
      "domain": "Games"
    },
    {
      "year": 2017.06,
      "logFlop": 19.97,
      "label": "MoE-Multi",
      "organization": "Jagiellonian University,Google Brain",
      "domain": "Language"
    },
    {
      "year": 2017.45,
      "logFlop": 18.87,
      "label": "Transformer",
      "organization": "Google Research,Google Brain",
      "domain": "Language"
    },
    {
      "year": 2017.52,
      "logFlop": 17.76,
      "label": "DeepLoc",
      "organization": "Technical University of Denmark,University of Copenhagen",
      "domain": "Biology"
    },
    {
      "year": 2017.52,
      "logFlop": 20.93,
      "label": "JFT",
      "organization": "Google Research,Carnegie Mellon University (CMU)",
      "domain": "Vision"
    },
    {
      "year": 2017.56,
      "logFlop": 19.75,
      "label": "ConvS2S (ensemble of 8 models)",
      "organization": "Meta AI",
      "domain": "Language"
    },
    {
      "year": 2017.6,
      "logFlop": 17.47,
      "label": "AWD-LSTM - 3-layer LSTM (tied) + continuous cache pointer (WT2)",
      "organization": "Salesforce Research",
      "domain": "Language"
    },
    {
      "year": 2017.6,
      "logFlop": 18.32,
      "label": "RetinaNet-R101",
      "organization": "Facebook AI Research",
      "domain": "Vision"
    },
    {
      "year": 2017.61,
      "logFlop": 20.78,
      "label": "OpenAI TI7 DOTA 1v1",
      "organization": "OpenAI",
      "domain": "Games"
    },
    {
      "year": 2017.62,
      "logFlop": 16.03,
      "label": "EI-REHN-1000D",
      "organization": "Korea Advanced Institute of Science and Technology (KAIST)",
      "domain": "Language"
    },
    {
      "year": 2017.63,
      "logFlop": 20.74,
      "label": "Libratus",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Games"
    },
    {
      "year": 2017.66,
      "logFlop": 17.66,
      "label": "GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)",
      "organization": "Ben-Gurion University of the Negev",
      "domain": "Language"
    },
    {
      "year": 2017.68,
      "logFlop": 15.37,
      "label": "PyramidNet",
      "organization": "Korea Advanced Institute of Science and Technology (KAIST)",
      "domain": "Vision"
    },
    {
      "year": 2017.71,
      "logFlop": 15.53,
      "label": "ISS",
      "organization": "Duke University,Microsoft",
      "domain": "Language"
    },
    {
      "year": 2017.74,
      "logFlop": 15.5,
      "label": "AWD-LSTM+WT+Cache+IOG (WT2)",
      "organization": "NTT Communication Science Laboratories",
      "domain": "Language"
    },
    {
      "year": 2017.8,
      "logFlop": 20.53,
      "label": "AlphaGo Master",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2017.8,
      "logFlop": 20.81,
      "label": "AlphaGo Zero",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2017.82,
      "logFlop": 17.49,
      "label": "Fraternal dropout + AWD-LSTM 3-layer (WT2)",
      "organization": "Jagiellonian University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2017.86,
      "logFlop": 18.53,
      "label": "AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Language"
    },
    {
      "year": 2017.93,
      "logFlop": 20.03,
      "label": "AlphaZero",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2018.09,
      "logFlop": 15.52,
      "label": "ELMo",
      "organization": "University of Washington,Allen Institute for AI",
      "domain": "Language"
    },
    {
      "year": 2018.09,
      "logFlop": 17.84,
      "label": "QRNN",
      "organization": "Salesforce Research",
      "domain": "Language"
    },
    {
      "year": 2018.1,
      "logFlop": 20.23,
      "label": "IMPALA",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2018.22,
      "logFlop": 17.77,
      "label": "4 layer QRNN (h=2500)",
      "organization": "Salesforce Research",
      "domain": "Language"
    },
    {
      "year": 2018.27,
      "logFlop": 19.13,
      "label": "YOLOv3",
      "organization": "University of Washington",
      "domain": "Vision"
    },
    {
      "year": 2018.33,
      "logFlop": 21.94,
      "label": "ResNeXt-101 32x48d",
      "organization": "Facebook",
      "domain": "Vision"
    },
    {
      "year": 2018.34,
      "logFlop": 17.1,
      "label": "Dropout-LSTM+Noise(Bernoulli) (WT2)",
      "organization": "Columbia University,New York University (NYU),Princeton University",
      "domain": "Language"
    },
    {
      "year": 2018.39,
      "logFlop": 16.86,
      "label": "aLSTM(depth-2)+RecurrentPolicy (WT2)",
      "organization": "University of Manchester,Alan Turing Institute",
      "domain": "Language"
    },
    {
      "year": 2018.42,
      "logFlop": 19.24,
      "label": "GPT-1",
      "organization": "OpenAI",
      "domain": "Language"
    },
    {
      "year": 2018.5,
      "logFlop": 19.54,
      "label": "FTW (For The Win)",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2018.52,
      "logFlop": 17.39,
      "label": "Big-Little Net",
      "organization": "IBM",
      "domain": "Vision"
    },
    {
      "year": 2018.52,
      "logFlop": 17.63,
      "label": "Big-Little Net (speech)",
      "organization": "IBM",
      "domain": "Speech"
    },
    {
      "year": 2018.66,
      "logFlop": 17.82,
      "label": "(ensemble): AWD-LSTM-DOC (fin) × 5 (WT2)",
      "organization": "NTT Communication Science Laboratories,Tohoku University",
      "domain": "Language"
    },
    {
      "year": 2018.66,
      "logFlop": 20.68,
      "label": "Big Transformer for Back-Translation",
      "organization": "Facebook AI Research,Google Brain",
      "domain": "Language"
    },
    {
      "year": 2018.71,
      "logFlop": 19.04,
      "label": "Transformer + Simple Recurrent Unit",
      "organization": "ASAPP,Cornell University,Google,Princeton University",
      "domain": "Language"
    },
    {
      "year": 2018.73,
      "logFlop": 14.99,
      "label": "LSTM+NeuralCache",
      "organization": "KU Leuven,ESAT - PSI,Apple",
      "domain": "Language"
    },
    {
      "year": 2018.74,
      "logFlop": 19.65,
      "label": "Transformer (Adaptive Input Embeddings) WT103",
      "organization": "Facebook AI Research",
      "domain": "Language"
    },
    {
      "year": 2018.78,
      "logFlop": 20.45,
      "label": "BERT-Large",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2018.79,
      "logFlop": 18.44,
      "label": "TrellisNet",
      "organization": "Carnegie Mellon University (CMU),Bosch Center for Artificial Intelligence,Intel Labs",
      "domain": "Language"
    },
    {
      "year": 2018.85,
      "logFlop": 19.84,
      "label": "Mesh-TensorFlow Transformer 2.9B (translation)",
      "organization": "Google Brain",
      "domain": "Language"
    },
    {
      "year": 2018.85,
      "logFlop": 20.21,
      "label": "Mesh-TensorFlow Transformer 4.9B (language)",
      "organization": "Google Brain",
      "domain": "Language"
    },
    {
      "year": 2018.87,
      "logFlop": 15.3,
      "label": "Multi-cell LSTM",
      "organization": "University of Hyderabad",
      "domain": "Language"
    },
    {
      "year": 2018.87,
      "logFlop": 16.71,
      "label": "Fine-tuned-AWD-LSTM-DOC (fin)",
      "organization": "Samsung R&D Institute Russia",
      "domain": "Language"
    },
    {
      "year": 2018.95,
      "logFlop": 16.59,
      "label": "StyleGAN",
      "organization": "NVIDIA",
      "domain": "Image generation"
    },
    {
      "year": 2019.02,
      "logFlop": 20.58,
      "label": "Transformer-XL (257M)",
      "organization": "Carnegie Mellon University (CMU),Google Brain",
      "domain": "Language"
    },
    {
      "year": 2019.09,
      "logFlop": 18.63,
      "label": "Hanabi 4 player",
      "organization": "DeepMind,University of Oxford,Carnegie Mellon University (CMU),Google Brain",
      "domain": "Games"
    },
    {
      "year": 2019.12,
      "logFlop": 21.28,
      "label": "GPT-2 (1.5B)",
      "organization": "OpenAI",
      "domain": "Language"
    },
    {
      "year": 2019.16,
      "logFlop": 19.37,
      "label": "KataGo",
      "organization": "Jane Street",
      "domain": "Games"
    },
    {
      "year": 2019.23,
      "logFlop": 19.95,
      "label": "SciBERT",
      "organization": "Allen Institute for AI",
      "domain": "Language"
    },
    {
      "year": 2019.26,
      "logFlop": 18.41,
      "label": "Cross-lingual alignment",
      "organization": "Tel Aviv University,Massachusetts Institute of Technology (MIT)",
      "domain": "Language"
    },
    {
      "year": 2019.27,
      "logFlop": 17.86,
      "label": "WeNet (Penn Treebank)",
      "organization": "Amazon",
      "domain": "Language"
    },
    {
      "year": 2019.3,
      "logFlop": 20.19,
      "label": "BERT-Large-CAS (PTB+WT2+WT103)",
      "organization": "Amazon",
      "domain": "Language"
    },
    {
      "year": 2019.32,
      "logFlop": 20.34,
      "label": "MuseNet",
      "organization": "OpenAI",
      "domain": "Audio"
    },
    {
      "year": 2019.37,
      "logFlop": 17.61,
      "label": "AWD-LSTM-DRILL + dynamic evaluation† (WT2)",
      "organization": "IDIAP",
      "domain": "Language"
    },
    {
      "year": 2019.41,
      "logFlop": 18.6,
      "label": "DLRM-2020",
      "organization": "Facebook AI",
      "domain": "Recommendation"
    },
    {
      "year": 2019.42,
      "logFlop": 20.58,
      "label": "Transformer-XL Large + Phrase Induction",
      "organization": "Massachusetts Institute of Technology (MIT),University of Illinois Urbana-Champaign (UIUC)",
      "domain": "Language"
    },
    {
      "year": 2019.42,
      "logFlop": 21.79,
      "label": "XLNet",
      "organization": "Carnegie Mellon University (CMU),Google Brain",
      "domain": "Language"
    },
    {
      "year": 2019.44,
      "logFlop": 17.5,
      "label": "AWD-LSTM + MoS + Partial Shuffled",
      "organization": "University of Texas at Austin",
      "domain": "Language"
    },
    {
      "year": 2019.5,
      "logFlop": 21.93,
      "label": "RoBERTa Large",
      "organization": "Facebook,University of Washington",
      "domain": "Language"
    },
    {
      "year": 2019.53,
      "logFlop": 16.82,
      "label": "Pluribus",
      "organization": "Facebook AI Research",
      "domain": "Games"
    },
    {
      "year": 2019.64,
      "logFlop": 19.58,
      "label": "trRosetta",
      "organization": "Nankai University,University of Washington,Tianjin University,Harvard University",
      "domain": "Biology"
    },
    {
      "year": 2019.68,
      "logFlop": 17.8,
      "label": "UDSMProt",
      "organization": "Fraunhofer Heinrich Hertz Institute",
      "domain": "Biology"
    },
    {
      "year": 2019.71,
      "logFlop": 21.96,
      "label": "Megatron-LM (8.3B)",
      "organization": "NVIDIA",
      "domain": "Language"
    },
    {
      "year": 2019.71,
      "logFlop": 22.05,
      "label": "Megatron-LM (1.2B)",
      "organization": "NVIDIA",
      "domain": "Language"
    },
    {
      "year": 2019.71,
      "logFlop": 22.34,
      "label": "Megatron-BERT",
      "organization": "NVIDIA",
      "domain": "Language"
    },
    {
      "year": 2019.75,
      "logFlop": 17.95,
      "label": "AlphaX-1",
      "organization": "Facebook AI Research,Brown University",
      "domain": "Vision"
    },
    {
      "year": 2019.75,
      "logFlop": 19.09,
      "label": "DistilBERT",
      "organization": "Hugging Face",
      "domain": "Language"
    },
    {
      "year": 2019.81,
      "logFlop": 21.95,
      "label": "T5-3B",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2019.81,
      "logFlop": 22.52,
      "label": "T5-11B",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2019.82,
      "logFlop": 23.03,
      "label": "AlphaStar",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2019.84,
      "logFlop": 19.48,
      "label": "Base LM + kNN LM + Continuous Cache",
      "organization": "Stanford University,Facebook AI Research",
      "domain": "Language"
    },
    {
      "year": 2019.85,
      "logFlop": 22.32,
      "label": "XLM-RoBERTa",
      "organization": "Facebook AI",
      "domain": "Language"
    },
    {
      "year": 2019.86,
      "logFlop": 19.37,
      "label": "Sandwich Transformer",
      "organization": "Allen Institute for AI,Facebook AI Research",
      "domain": "Language"
    },
    {
      "year": 2019.86,
      "logFlop": 20.92,
      "label": "CamemBERT",
      "organization": "Facebook,INRIA,Sorbonne University",
      "domain": "Language"
    },
    {
      "year": 2019.86,
      "logFlop": 22.42,
      "label": "Noisy Student (L2)",
      "organization": "Carnegie Mellon University (CMU),Google",
      "domain": "Vision"
    },
    {
      "year": 2019.88,
      "logFlop": 19.68,
      "label": "MuZero",
      "organization": "DeepMind",
      "domain": "Games"
    },
    {
      "year": 2019.91,
      "logFlop": 18.24,
      "label": "Transformer-XL DeFINE (141M)",
      "organization": "University of Washington,Allen Institute for AI",
      "domain": "Language"
    },
    {
      "year": 2019.93,
      "logFlop": 16.77,
      "label": "MMLSTM (PTB)",
      "organization": "Beijing University of Posts and Telecommunications,University of West London",
      "domain": "Language"
    },
    {
      "year": 2019.93,
      "logFlop": 17.29,
      "label": "MMLSTM (WT-2)",
      "organization": "Beijing University of Posts and Telecommunications,University of West London",
      "domain": "Language"
    },
    {
      "year": 2019.95,
      "logFlop": 22.11,
      "label": "OpenAI Five Rerun",
      "organization": "OpenAI",
      "domain": "Games"
    },
    {
      "year": 2019.95,
      "logFlop": 22.83,
      "label": "OpenAI Five",
      "organization": "OpenAI",
      "domain": "Games"
    },
    {
      "year": 2019.97,
      "logFlop": 20.89,
      "label": "DD-PPO",
      "organization": "Georgia Institute of Technology,Facebook AI Research,Oregon State University,Simon Fraser University",
      "domain": "Robotics"
    },
    {
      "year": 2020.04,
      "logFlop": 20.0,
      "label": "AlphaFold",
      "organization": "DeepMind",
      "domain": "Biology"
    },
    {
      "year": 2020.05,
      "logFlop": 21.91,
      "label": "ContextNet + Noisy Student",
      "organization": "Google",
      "domain": "Speech"
    },
    {
      "year": 2020.08,
      "logFlop": 23.05,
      "label": "Meena",
      "organization": "Google Brain",
      "domain": "Language"
    },
    {
      "year": 2020.11,
      "logFlop": 19.43,
      "label": "TaLK Convolution",
      "organization": "Carleton University",
      "domain": "Language"
    },
    {
      "year": 2020.11,
      "logFlop": 21.38,
      "label": "ALBERT-xxlarge",
      "organization": "Toyota Technological Institute at Chicago,Google",
      "domain": "Language"
    },
    {
      "year": 2020.12,
      "logFlop": 19.53,
      "label": "FFN SwiGLU",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2020.12,
      "logFlop": 22.2,
      "label": "Turing-NLG",
      "organization": "Microsoft",
      "domain": "Language"
    },
    {
      "year": 2020.14,
      "logFlop": 18.89,
      "label": "Feedback Transformer",
      "organization": "LORIA,University of Lorraine,Facebook AI Research",
      "domain": "Language"
    },
    {
      "year": 2020.19,
      "logFlop": 19.42,
      "label": "TransformerXL + spectrum control",
      "organization": "University of California Los Angeles (UCLA),JD.com",
      "domain": "Language"
    },
    {
      "year": 2020.21,
      "logFlop": 18.2,
      "label": "Tensor-Transformer(1core)+PN (WT103)",
      "organization": "University of California (UC) Berkeley",
      "domain": "Language"
    },
    {
      "year": 2020.23,
      "logFlop": 18.98,
      "label": "MetNet",
      "organization": "Google",
      "domain": "Earth science"
    },
    {
      "year": 2020.23,
      "logFlop": 21.49,
      "label": "ELECTRA",
      "organization": "Stanford University,Google,Google Brain",
      "domain": "Language"
    },
    {
      "year": 2020.33,
      "logFlop": 20.79,
      "label": "Once for All",
      "organization": "MIT-IBM Watson AI Lab,Massachusetts Institute of Technology (MIT),IBM",
      "domain": "Vision"
    },
    {
      "year": 2020.34,
      "logFlop": 19.22,
      "label": "UnifiedQA",
      "organization": "Allen Institute for AI,University of Washington",
      "domain": "Language"
    },
    {
      "year": 2020.4,
      "logFlop": 20.6,
      "label": "DETR",
      "organization": "Facebook",
      "domain": "Vision"
    },
    {
      "year": 2020.41,
      "logFlop": 23.5,
      "label": "GPT-3 175B (davinci)",
      "organization": "OpenAI",
      "domain": "Language"
    },
    {
      "year": 2020.49,
      "logFlop": 22.68,
      "label": "GShard (dense)",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2020.59,
      "logFlop": 18.58,
      "label": "DeLighT",
      "organization": "University of Washington,Allen Institute for AI,Facebook AI Research",
      "domain": "Language"
    },
    {
      "year": 2020.6,
      "logFlop": 20.3,
      "label": "ERNIE-GEN (large)",
      "organization": "Baidu",
      "domain": "Language"
    },
    {
      "year": 2020.67,
      "logFlop": 18.99,
      "label": "ProBERTa",
      "organization": "University of Illinois Urbana-Champaign (UIUC),Reed College",
      "domain": "Biology"
    },
    {
      "year": 2020.75,
      "logFlop": 22.26,
      "label": "LUKE",
      "organization": "University of Washington,National Institute of Informatics",
      "domain": "Language"
    },
    {
      "year": 2020.8,
      "logFlop": 21.88,
      "label": "Conformer + Wav2vec 2.0 + Noisy Student",
      "organization": "Google,Google Research,Google Brain",
      "domain": "Speech"
    },
    {
      "year": 2020.8,
      "logFlop": 22.91,
      "label": "mT5-XXL",
      "organization": "Google,Google Research",
      "domain": "Language"
    },
    {
      "year": 2020.81,
      "logFlop": 21.15,
      "label": "German ELECTRA Large",
      "organization": "deepset,Bayerische Staatsbibliothek Muenchen",
      "domain": "Language"
    },
    {
      "year": 2020.81,
      "logFlop": 21.59,
      "label": "wave2vec 2.0 LARGE",
      "organization": "Facebook",
      "domain": "Speech"
    },
    {
      "year": 2020.81,
      "logFlop": 21.63,
      "label": "ViT-Huge/14",
      "organization": "Google Brain,Google Research",
      "domain": "Vision"
    },
    {
      "year": 2020.9,
      "logFlop": 21.22,
      "label": "KEPLER",
      "organization": "Tsinghua University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),HEC,CIFAR AI Research,Princeton University,University of Montreal / Université de Montréal",
      "domain": "Language"
    },
    {
      "year": 2020.91,
      "logFlop": 21.48,
      "label": "AlphaFold 2",
      "organization": "DeepMind",
      "domain": "Biology"
    },
    {
      "year": 2020.92,
      "logFlop": 20.42,
      "label": "CPM-Large",
      "organization": "Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI",
      "domain": "Language"
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      "year": 2020.96,
      "logFlop": 21.71,
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      "organization": "Facebook AI Research,New York University (NYU)",
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      "year": 2020.98,
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      "organization": "Korea University,Princeton University",
      "domain": "Language"
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      "year": 2020.99,
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      "label": "ERNIE-Doc (247M)",
      "organization": "Baidu",
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      "year": 2021.01,
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      "year": 2021.03,
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      "label": "Switch",
      "organization": "Google",
      "domain": "Language"
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      "year": 2021.04,
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      "organization": "Meta AI,Sorbonne University",
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      "year": 2021.11,
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      "organization": "University of Washington,Microsoft Research",
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      "year": 2021.12,
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      "label": "MSA Transformer",
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      "year": 2021.15,
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      "year": 2021.16,
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      "label": "Meta Pseudo Labels",
      "organization": "Google Brain,Google AI",
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      "year": 2021.3,
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      "organization": "Alibaba",
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      "organization": "Technical University of Munich,Med AI Technology,NVIDIA,Oak Ridge National Laboratory,Google,Seoul National University",
      "domain": "Biology"
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      "year": 2021.34,
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      "year": 2021.36,
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      "organization": "OpenAI",
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      "year": 2021.38,
      "logFlop": 18.98,
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      "year": 2021.44,
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      "year": 2021.48,
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      "organization": "Baidu",
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      "organization": "OpenAI",
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      "year": 2021.57,
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      "organization": "Facebook AI Research",
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      "year": 2021.57,
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      "organization": "Facebook AI Research,INRIA",
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    {
      "year": 2021.57,
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      "organization": "DeepMind",
      "domain": "Games"
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      "year": 2021.6,
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      "organization": "Megvii Inc",
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      "year": 2021.61,
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      "label": "Zidong Taichu",
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      "year": 2021.61,
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      "year": 2021.62,
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      "organization": "Northeastern University",
      "domain": "Biology"
    },
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      "year": 2021.63,
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      "label": "XLMR-XXL",
      "organization": "Facebook AI Research",
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      "year": 2021.67,
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      "organization": "Google Research",
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      "organization": "NAVER",
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      "year": 2021.72,
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      "year": 2021.74,
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      "organization": "Microsoft",
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      "year": 2021.76,
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      "organization": "Google DeepMind,DeepMind",
      "domain": "Biology"
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      "year": 2021.78,
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      "organization": "Inspur",
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      "year": 2021.78,
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      "domain": "Language"
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      "year": 2021.82,
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      "domain": "Language"
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      "year": 2021.84,
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      "organization": "Heidelberg University",
      "domain": "Image generation"
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      "year": 2021.84,
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      "organization": "Salesforce,Nanyang Technological University",
      "domain": "Language"
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      "year": 2021.86,
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      "label": "Masked Autoencoders ViT-H",
      "organization": "Facebook AI Research",
      "domain": "Vision"
    },
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      "year": 2021.88,
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    },
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      "year": 2021.88,
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      "organization": "Google",
      "domain": "Vision"
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      "year": 2021.89,
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      "label": "Florence",
      "organization": "Microsoft",
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      "year": 2021.9,
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      "organization": "Microsoft Research,Peking University",
      "domain": "Multimodal,Vision,Image generation,Video,Language"
    },
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      "year": 2021.93,
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      "organization": "DeepMind",
      "domain": "Language"
    },
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      "organization": "Google",
      "domain": "Language"
    },
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      "domain": "Language"
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      "organization": "Meta AI,Facebook AI Research",
      "domain": "Language"
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      "organization": "Meta AI,University of Texas at Austin",
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      "organization": "OpenAI",
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    {
      "year": 2022.09,
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      "label": "AlphaCode",
      "organization": "DeepMind",
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      "organization": "DeepMind",
      "domain": "Language"
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      "year": 2022.11,
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      "label": "ProteinBERT",
      "organization": "Hebrew University of Jerusalem,Ben-Gurion University of the Negev,Deep Trading",
      "domain": "Biology"
    },
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      "year": 2022.11,
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      "organization": "EleutherAI",
      "domain": "Language"
    },
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      "year": 2022.11,
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      "label": "LaMDA",
      "organization": "Google",
      "domain": "Language"
    },
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      "organization": "Google,Google Brain,Google Research",
      "domain": "Language"
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      "label": "FourCastNet",
      "organization": "NVIDIA,NERSC, Lawrence Berkeley National Laboratory,University of Michigan,Rice University,California Institute of Technology,Purdue University",
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    },
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      "domain": "Language"
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      "organization": "OpenAI",
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      "domain": "Vision"
    },
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      "organization": "OpenAI",
      "domain": "Language"
    },
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      "domain": "Language"
    },
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      "year": 2022.23,
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      "label": "Make-A-Scene",
      "organization": "Meta AI",
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    },
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      "year": 2022.24,
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      "label": "Chinchilla",
      "organization": "DeepMind",
      "domain": "Language"
    },
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      "year": 2022.26,
      "logFlop": 23.53,
      "label": "DALL·E 2",
      "organization": "OpenAI",
      "domain": "Image generation"
    },
    {
      "year": 2022.26,
      "logFlop": 24.4,
      "label": "PaLM (540B)",
      "organization": "Google Research",
      "domain": "Language"
    },
    {
      "year": 2022.27,
      "logFlop": 20.15,
      "label": "BERT-RBP",
      "organization": "Waseda University",
      "domain": "Biology"
    },
    {
      "year": 2022.28,
      "logFlop": 20.73,
      "label": "Sparse all-MLP",
      "organization": "Meta AI",
      "domain": "Language"
    },
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      "year": 2022.28,
      "logFlop": 22.7,
      "label": "Stable Diffusion (LDM-KL-8-G)",
      "organization": "Runway,Ludwig Maximilian University of Munich,Heidelberg University",
      "domain": "Image generation"
    },
    {
      "year": 2022.32,
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      "organization": "DeepMind",
      "domain": "Multimodal,Vision,Language,Video"
    },
    {
      "year": 2022.33,
      "logFlop": 23.63,
      "label": "OPT-175B",
      "organization": "Meta AI",
      "domain": "Language"
    },
    {
      "year": 2022.36,
      "logFlop": 21.6,
      "label": "Gato",
      "organization": "DeepMind",
      "domain": "Multimodal,Robotics,Games,Language"
    },
    {
      "year": 2022.36,
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      "organization": "Google Research,Google Brain",
      "domain": "Language"
    },
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      "year": 2022.39,
      "logFlop": 22.16,
      "label": "Imagen",
      "organization": "Google Brain",
      "domain": "Image generation"
    },
    {
      "year": 2022.4,
      "logFlop": 20.95,
      "label": "GPT-2 Medium (FlashAttention)",
      "organization": "Stanford University,University at Buffalo",
      "domain": "Language"
    },
    {
      "year": 2022.4,
      "logFlop": 21.86,
      "label": "Tranception",
      "organization": "University of Oxford,Harvard Medical School,Cohere",
      "domain": "Biology"
    },
    {
      "year": 2022.43,
      "logFlop": 18.52,
      "label": "DITTO",
      "organization": "Tsinghua University,Apple,Westlake University,Chinese University of Hong Kong (CUHK)",
      "domain": "Language"
    },
    {
      "year": 2022.45,
      "logFlop": 22.86,
      "label": "CoCa",
      "organization": "Google Research",
      "domain": "Vision"
    },
    {
      "year": 2022.47,
      "logFlop": 23.71,
      "label": "Parti",
      "organization": "Google Research",
      "domain": "Image generation"
    },
    {
      "year": 2022.49,
      "logFlop": 22.13,
      "label": "ProGen2-xlarge",
      "organization": "Salesforce Research,Columbia University,Johns Hopkins University",
      "domain": "Biology"
    },
    {
      "year": 2022.49,
      "logFlop": 24.44,
      "label": "Minerva (540B)",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2022.51,
      "logFlop": 21.43,
      "label": "CodeT5-large",
      "organization": "Salesforce",
      "domain": "Language"
    },
    {
      "year": 2022.51,
      "logFlop": 22.24,
      "label": "NLLB",
      "organization": "Meta AI",
      "domain": "Language"
    },
    {
      "year": 2022.53,
      "logFlop": 23.56,
      "label": "BLOOM-176B",
      "organization": "Hugging Face,BigScience",
      "domain": "Language"
    },
    {
      "year": 2022.55,
      "logFlop": 22.87,
      "label": "ESM2-15B",
      "organization": "Meta AI,New York University (NYU),Stanford University,Massachusetts Institute of Technology (MIT)",
      "domain": "Biology"
    },
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      "year": 2022.56,
      "logFlop": 22.01,
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      "organization": "Massachusetts Institute of Technology (MIT),Westlake University",
      "domain": "Biology"
    },
    {
      "year": 2022.59,
      "logFlop": 23.31,
      "label": "AlexaTM 20B",
      "organization": "Amazon",
      "domain": "Language"
    },
    {
      "year": 2022.59,
      "logFlop": 23.55,
      "label": "GLM-130B",
      "organization": "Tsinghua University",
      "domain": "Language"
    },
    {
      "year": 2022.61,
      "logFlop": 23.63,
      "label": "BlenderBot 3",
      "organization": "McGill University,Meta AI,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms)",
      "domain": "Language"
    },
    {
      "year": 2022.64,
      "logFlop": 19.85,
      "label": "BEIT-3",
      "organization": "Microsoft",
      "domain": "Multimodal,Vision,Language"
    },
    {
      "year": 2022.7,
      "logFlop": 23.23,
      "label": "PaLI",
      "organization": "Google",
      "domain": "Language,Vision,Multimodal"
    },
    {
      "year": 2022.72,
      "logFlop": 21.62,
      "label": "Whisper",
      "organization": "OpenAI",
      "domain": "Speech"
    },
    {
      "year": 2022.76,
      "logFlop": 19.86,
      "label": "DiffDock",
      "organization": "Massachusetts Institute of Technology (MIT)",
      "domain": "Biology"
    },
    {
      "year": 2022.76,
      "logFlop": 20.85,
      "label": "AlphaTensor",
      "organization": "DeepMind",
      "domain": "Other,Games,Mathematics"
    },
    {
      "year": 2022.78,
      "logFlop": 21.15,
      "label": "GenSLM",
      "organization": "University of Chicago,NVIDIA,Harvard University,Cerebras Systems,Technical University of Munich,California Institute of Technology",
      "domain": "Biology"
    },
    {
      "year": 2022.8,
      "logFlop": 24.4,
      "label": "U-PaLM (540B)",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2022.8,
      "logFlop": 24.4,
      "label": "Flan-PaLM 540B",
      "organization": "Google",
      "domain": "Language"
    },
    {
      "year": 2022.84,
      "logFlop": 17.15,
      "label": "Mogrifier RLSTM (WT2)",
      "organization": "DeepMind",
      "domain": "Language"
    },
    {
      "year": 2022.84,
      "logFlop": 19.74,
      "label": "eDiff-I",
      "organization": "NVIDIA",
      "domain": "Image generation"
    },
    {
      "year": 2022.86,
      "logFlop": 21.38,
      "label": "InternImage",
      "organization": "Shanghai AI Lab,Tsinghua University,Nanjing University,SenseTime,Chinese University of Hong Kong (CUHK)",
      "domain": "Vision"
    },
    {
      "year": 2022.87,
      "logFlop": 22.18,
      "label": "EVA-01",
      "organization": "Beijing Academy of Artificial Intelligence / BAAI,Huazhong University of Science and Technology,Zhejiang University (ZJU),Beijing Institute of Technology",
      "domain": "Vision"
    },
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      "year": 2022.88,
      "logFlop": 20.11,
      "label": "Fusion in Encoder",
      "organization": "Samsung",
      "domain": "Language"
    },
    {
      "year": 2022.88,
      "logFlop": 23.51,
      "label": "Galactica",
      "organization": "Meta AI",
      "domain": "Language,Biology"
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      "year": 2022.89,
      "logFlop": 20.71,
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      "organization": "Alibaba,University of Waterloo,Vector Institute",
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      "year": 2022.91,
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      "year": 2022.93,
      "logFlop": 22.89,
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      "organization": "Wuhan University,JD Explore Academy,Shanghai AI Lab,Nanyang Technological University,Washington University in St Louis,Chongqing University of Posts and Telecommunications,University of Sydney",
      "domain": "Language"
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      "year": 2022.97,
      "logFlop": 19.46,
      "label": "CaLM",
      "organization": "University of Oxford",
      "domain": "Biology"
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      "year": 2022.99,
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      "organization": "Stanford University,University at Buffalo",
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      "year": 2023.01,
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      "organization": "Microsoft",
      "domain": "Audio,Speech"
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      "year": 2023.03,
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      "label": "DreamerV3",
      "organization": "DeepMind,University of Toronto",
      "domain": "Games"
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      "year": 2023.04,
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      "organization": "Technical University of Munich,Columbia University",
      "domain": "Biology"
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      "year": 2023.04,
      "logFlop": 21.91,
      "label": "Nucleotide Transformer",
      "organization": "NVIDIA,Technical University of Munich,InstaDeep",
      "domain": "Biology"
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      "year": 2023.07,
      "logFlop": 20.54,
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      "organization": "Utrecht University",
      "domain": "Image generation"
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      "year": 2023.08,
      "logFlop": 21.08,
      "label": "BLIP-2 (Q-Former)",
      "organization": "Salesforce Research",
      "domain": "Vision,Language"
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      "year": 2023.11,
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      "label": "ViT-22B",
      "organization": "Google",
      "domain": "Vision"
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      "year": 2023.15,
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      "label": "LLaMA-65B",
      "organization": "Meta AI",
      "domain": "Language"
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      "year": 2023.17,
      "logFlop": 20.78,
      "label": "DiT-XL/2",
      "organization": "New York University (NYU),University of California (UC) Berkeley",
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      "year": 2023.18,
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      "label": "AudioGen",
      "organization": "Meta AI,Hebrew University of Jerusalem",
      "domain": "Audio"
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      "year": 2023.2,
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      "organization": "Technology Innovation Institute",
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      "year": 2023.2,
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      "organization": "OpenAI",
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      "organization": "Huawei Noah's Ark Lab",
      "domain": "Language"
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      "organization": "Google DeepMind",
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      "organization": "Nanjing University,Shenzhen Institute of Advanced Technology,Shanghai AI Lab",
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      "year": 2023.24,
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      "organization": "Bloomberg,Johns Hopkins University",
      "domain": "Language"
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      "organization": "Meta AI",
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      "organization": "Facebook AI Research,University of Washington,University of California (UC) Berkeley,Carnegie Mellon University (CMU),Toyota Technological Institute at Chicago",
      "domain": "Language"
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      "year": 2023.28,
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      "domain": "Vision"
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      "year": 2023.29,
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      "label": "LLaVA",
      "organization": "University of Wisconsin Madison,Microsoft Research,Columbia University",
      "domain": "Multimodal,Vision,Language"
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      "organization": "Hugging Face,ServiceNow,Northeastern University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),Carnegie Mellon University (CMU),Johns Hopkins University,Leipzig University,ScaDS.AI,Queen Mary University of London,Roblox,Sea AI Lab,Technion - Israel Institute of Technology,Monash University,CSIRO,Data61,McGill University,Saama,University of British Columbia (UBC),Massachusetts Institute of Technology (MIT),Technical University of Munich,IBM,University of Vermont,UnfoldML,SAP,University of Notre Dame,Columbia University,New York University (NYU),University of Allahabad,Discover Dollar,Toloka,Telefonica,Stanford University,Weizmann Institute of Science,Alan Turing Institute,Wellesley College,EleutherAI,Forschungszentrum Julich",
      "domain": "Language"
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      "year": 2023.36,
      "logFlop": 20.29,
      "label": "InstructBLIP",
      "organization": "Salesforce Research,Hong Kong University of Science and Technology (HKUST),Nanyang Technological University",
      "domain": "Multimodal,Language,Vision"
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      "year": 2023.36,
      "logFlop": 24.87,
      "label": "PaLM 2",
      "organization": "Google",
      "domain": "Language"
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      "year": 2023.38,
      "logFlop": 20.26,
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      "organization": "Alibaba,Huazhong University of Science and Technology",
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      "organization": "Google Research",
      "domain": "Multimodal,Language,Vision,Video"
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      "logFlop": 21.26,
      "label": "HyenaDNA",
      "organization": "Stanford University,Harvard University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal",
      "domain": "Biology"
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      "year": 2023.51,
      "logFlop": 22.6,
      "label": "Pangu-Weather",
      "organization": "Huawei",
      "domain": "Earth science"
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      "label": "xTrimoPGLM -100B",
      "organization": "Tsinghua University,BioMap Research",
      "domain": "Biology"
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      "year": 2023.51,
      "logFlop": 24.0,
      "label": "InternLM",
      "organization": "Shanghai AI Lab,SenseTime",
      "domain": "Language"
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      "label": "Claude 2",
      "organization": "Anthropic",
      "domain": "Language"
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      "year": 2023.55,
      "logFlop": 22.92,
      "label": "Llama 2-7B",
      "organization": "Meta AI",
      "domain": "Language"
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      "year": 2023.55,
      "logFlop": 23.91,
      "label": "Llama 2-70B",
      "organization": "Meta AI",
      "domain": "Language"
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      "year": 2023.57,
      "logFlop": 18.59,
      "label": "AudioLM",
      "organization": "Google Research",
      "domain": "Audio"
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      "year": 2023.59,
      "logFlop": 21.88,
      "label": "GGNN",
      "organization": "Westlake University,Tsinghua University,Toyota Technological Institute at Chicago",
      "domain": "Biology"
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      "year": 2023.66,
      "logFlop": 16.69,
      "label": "PeptideBERT",
      "organization": "Carnegie Mellon University (CMU)",
      "domain": "Biology"
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      "year": 2023.66,
      "logFlop": 16.73,
      "label": "Swift",
      "organization": "Intel Labs",
      "domain": "Robotics"
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      "logFlop": 22.69,
      "label": "Jais",
      "organization": "Cerebras Systems,Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Inception G42",
      "domain": "Language"
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      "year": 2023.68,
      "logFlop": 24.58,
      "label": "Falcon-180B",
      "organization": "Technology Innovation Institute",
      "domain": "Language"
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      "year": 2023.74,
      "logFlop": 24.68,
      "label": "Amazon Titan",
      "organization": "Amazon",
      "domain": "Language,Image generation"
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      "year": 2023.77,
      "logFlop": 20.33,
      "label": "RoseTTAFold All-Atom (RFAA)",
      "organization": "University of Washington,Seoul National University,University of Sheffield",
      "domain": "Biology"
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      "year": 2023.77,
      "logFlop": 23.2,
      "label": "FinGPT-13B",
      "organization": "University of California Los Angeles (UCLA),Columbia University,New York University (NYU)",
      "domain": "Language"
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      "year": 2023.82,
      "logFlop": 18.9,
      "label": "CODEFUSION (Python)",
      "organization": "Microsoft,Microsoft Research",
      "domain": "Language"
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      "year": 2023.82,
      "logFlop": 22.7,
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      "organization": "Z.ai (Zhipu AI)",
      "domain": "Multimodal,Language,Vision"
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      "year": 2023.82,
      "logFlop": 23.4,
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      "organization": "Kunlun Inc.",
      "domain": "Language"
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      "year": 2023.84,
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      "label": "Yi-34B",
      "organization": "01.AI",
      "domain": "Language"
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      "year": 2023.84,
      "logFlop": 24.46,
      "label": "Grok-1",
      "organization": "xAI",
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      "year": 2023.85,
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      "label": "RoFormer",
      "organization": "Zhuiyi Technology",
      "domain": "Language"
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      "label": "MultiBand Diffusion",
      "organization": "Meta AI,Hebrew University of Jerusalem,LORIA",
      "domain": "Audio,Speech"
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      "logFlop": 22.8,
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      "organization": "Tsinghua University,Z.ai (Zhipu AI),Beihang University",
      "domain": "Multimodal,Vision,Language"
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      "organization": "University of Wisconsin Madison,Microsoft Research",
      "domain": "Multimodal,Language,Vision"
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      "year": 2023.87,
      "logFlop": 22.32,
      "label": "GraphCast",
      "organization": "Google DeepMind",
      "domain": "Earth science"
    },
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      "year": 2023.87,
      "logFlop": 22.48,
      "label": "SPHINX (Llama 2 13B)",
      "organization": "Shanghai AI Lab,Chinese University of Hong Kong (CUHK),ShanghaiTech University",
      "domain": "Vision,Language,Multimodal"
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      "year": 2023.87,
      "logFlop": 22.66,
      "label": "Volcano 13B",
      "organization": "Korea University,Korea Advanced Institute of Science and Technology (KAIST),LG",
      "domain": "Language,Multimodal,Vision"
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      "year": 2023.87,
      "logFlop": 23.26,
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      "organization": "NVIDIA",
      "domain": "Language"
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      "year": 2023.89,
      "logFlop": 25.0,
      "label": "Inflection-2",
      "organization": "Inflection AI",
      "domain": "Language"
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      "year": 2023.91,
      "logFlop": 24.11,
      "label": "Qwen-72B",
      "organization": "Alibaba",
      "domain": "Language"
    },
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      "year": 2023.93,
      "logFlop": 23.2,
      "label": "Llama Guard",
      "organization": "Meta AI",
      "domain": "Language"
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      "year": 2023.93,
      "logFlop": 25.7,
      "label": "Gemini 1.0 Ultra",
      "organization": "Google DeepMind",
      "domain": "Multimodal,Language,Vision"
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      "logFlop": 21.36,
      "label": "VILA-13B",
      "organization": "NVIDIA,Massachusetts Institute of Technology (MIT)",
      "domain": "Multimodal,Language,Vision"
    },
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      "year": 2023.95,
      "logFlop": 22.83,
      "label": "CogAgent",
      "organization": "Tsinghua University,Z.ai (Zhipu AI)",
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    },
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      "year": 2023.95,
      "logFlop": 23.59,
      "label": "FunSearch",
      "organization": "Google DeepMind",
      "domain": "Language,Search"
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      "year": 2023.95,
      "logFlop": 23.89,
      "label": "Mixtral 8x7B",
      "organization": "Mistral AI",
      "domain": "Language"
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      "logFlop": 23.41,
      "label": "nekomata-14b",
      "organization": "rinna",
      "domain": "Language"
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      "organization": "Google Research",
      "domain": "Language"
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      "logFlop": 24.11,
      "label": "Qwen1.5-72B",
      "organization": "Alibaba",
      "domain": "Language"
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      "organization": "Stability AI",
      "domain": "Image generation"
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      "label": "MegaScale (Production)",
      "organization": "ByteDance,Peking University",
      "domain": "Language"
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      "logFlop": 25.05,
      "label": "Mistral Large",
      "organization": "Mistral AI",
      "domain": "Language"
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      "logFlop": 25.02,
      "label": "Aramco Metabrain AI",
      "organization": "Saudi Aramco",
      "domain": "Language"
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      "logFlop": 24.9,
      "label": "Inflection-2.5",
      "organization": "Inflection AI",
      "domain": "Language"
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      "year": 2024.2,
      "logFlop": 23.69,
      "label": "MM1-30B",
      "organization": "Apple",
      "domain": "Multimodal,Language,Vision"
    },
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      "year": 2024.24,
      "logFlop": 24.41,
      "label": "DBRX",
      "organization": "Databricks",
      "domain": "Language"
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      "year": 2024.29,
      "logFlop": 24.92,
      "label": "Reka Core",
      "organization": "Reka AI",
      "domain": "Multimodal,Language,Vision,Video,Speech"
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      "organization": "Meta AI",
      "domain": "Language"
    },
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      "year": 2024.33,
      "logFlop": 20.91,
      "label": "GenCast",
      "organization": "Google DeepMind",
      "domain": "Earth science"
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      "label": "VILA1.5-13B",
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      "domain": "Multimodal,Language,Vision,Video"
    },
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      "logFlop": 22.62,
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      "domain": "Biology"
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      "label": "Yi-Large",
      "organization": "01.AI",
      "domain": "Language"
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      "logFlop": 20.77,
      "label": "Octo-Base",
      "organization": "University of California (UC) Berkeley,Stanford University,Carnegie Mellon University (CMU),DeepMind",
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      "logFlop": 24.03,
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      "organization": "Saudi Data and Artificial Intelligence Authority",
      "domain": "Language"
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      "year": 2024.43,
      "logFlop": 24.48,
      "label": "Qwen2-72B",
      "organization": "Alibaba",
      "domain": "Language"
    },
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      "logFlop": 23.04,
      "label": "OpenVLA",
      "organization": "Stanford University,University of California (UC) Berkeley,Toyota Research Institute,Google DeepMind,Massachusetts Institute of Technology (MIT),Physical Intelligence",
      "domain": "Robotics,Vision,Language"
    },
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      "year": 2024.45,
      "logFlop": 24.9,
      "label": "Llama-3.1-Nemotron-70B-Instruct",
      "organization": "NVIDIA,Meta AI",
      "domain": "Language"
    },
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      "year": 2024.45,
      "logFlop": 25.26,
      "label": "Nemotron-4 340B",
      "organization": "NVIDIA",
      "domain": "Language"
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      "year": 2024.46,
      "logFlop": 24.11,
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      "organization": "DeepSeek",
      "domain": "Language"
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      "domain": "Multimodal,Language,Vision"
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      "logFlop": 24.03,
      "label": "ESM3 (98B)",
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      "domain": "Biology"
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      "logFlop": 25.33,
      "label": "Mistral Large 2",
      "organization": "Mistral AI",
      "domain": "Language"
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      "year": 2024.56,
      "logFlop": 25.58,
      "label": "Llama 3.1-405B",
      "organization": "Meta AI",
      "domain": "Language"
    },
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      "year": 2024.57,
      "logFlop": 23.65,
      "label": "AFM-on-device",
      "organization": "Apple",
      "domain": "Language"
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      "year": 2024.57,
      "logFlop": 24.63,
      "label": "AFM-server",
      "organization": "Apple",
      "domain": "Language"
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      "logFlop": 24.48,
      "label": "LLaVA-OV-72B",
      "organization": "ByteDance,Nanyang Technological University,Chinese University of Hong Kong (CUHK),Hong Kong University of Science and Technology (HKUST)",
      "domain": "Multimodal,Vision,Language,Video"
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      "logFlop": 25.47,
      "label": "Grok-2",
      "organization": "xAI",
      "domain": "Language,Vision,Multimodal"
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      "logFlop": 24.25,
      "label": "DeepSeek-V2.5",
      "organization": "DeepSeek",
      "domain": "Language"
    },
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      "year": 2024.71,
      "logFlop": 24.55,
      "label": "Qwen2.5-32B",
      "organization": "Alibaba",
      "domain": "Language"
    },
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      "year": 2024.72,
      "logFlop": 24.84,
      "label": "Telechat2-115B",
      "organization": "China Telecom",
      "domain": "Language"
    },
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      "year": 2024.72,
      "logFlop": 24.89,
      "label": "Qwen2.5-72B",
      "organization": "Alibaba",
      "domain": "Language"
    },
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      "year": 2024.72,
      "logFlop": 24.89,
      "label": "Qwen2.5 Instruct (72B)",
      "organization": "Alibaba",
      "domain": "Language"
    },
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      "year": 2024.73,
      "logFlop": 23.76,
      "label": "Llama 3.2 11B",
      "organization": "Meta AI",
      "domain": "Multimodal,Vision,Language"
    },
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      "year": 2024.76,
      "logFlop": 24.22,
      "label": "Movie Gen Video",
      "organization": "Meta AI",
      "domain": "Video,Vision"
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      "year": 2024.78,
      "logFlop": 22.61,
      "label": "RDT-1B",
      "organization": "Tsinghua University",
      "domain": "Robotics"
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      "year": 2024.79,
      "logFlop": 21.89,
      "label": "CHAI-1",
      "organization": "Chai discovery",
      "domain": "Biology"
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      "year": 2024.8,
      "logFlop": 24.18,
      "label": "Yi-Lightning",
      "organization": "01.AI",
      "domain": "Language"
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      "year": 2024.81,
      "logFlop": 24.48,
      "label": "NVLM-X 72B",
      "organization": "NVIDIA",
      "domain": "Vision,Language"
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      "year": 2024.81,
      "logFlop": 24.48,
      "label": "NVLM-H 72B",
      "organization": "NVIDIA",
      "domain": "Vision,Language"
    },
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      "year": 2024.81,
      "logFlop": 24.48,
      "label": "NVLM-D 72B",
      "organization": "NVIDIA",
      "domain": "Vision,Language"
    },
    {
      "year": 2024.83,
      "logFlop": 25.4,
      "label": "Doubao-pro",
      "organization": "ByteDance",
      "domain": "Language"
    },
    {
      "year": 2024.85,
      "logFlop": 24.54,
      "label": "Hunyuan-Large",
      "organization": "Tencent",
      "domain": "Language"
    },
    {
      "year": 2024.92,
      "logFlop": 24.78,
      "label": "Amazon Nova Pro",
      "organization": "Amazon",
      "domain": "Multimodal,Language,Video,Vision"
    },
    {
      "year": 2024.93,
      "logFlop": 24.84,
      "label": "Llama 3.3 70B",
      "organization": "Meta AI",
      "domain": "Language"
    },
    {
      "year": 2024.94,
      "logFlop": 24.1,
      "label": "EXAONE 3.5 32B",
      "organization": "LG AI Research",
      "domain": "Language"
    },
    {
      "year": 2024.98,
      "logFlop": 24.52,
      "label": "DeepSeek-V3",
      "organization": "DeepSeek",
      "domain": "Language"
    },
    {
      "year": 2025.05,
      "logFlop": 22.67,
      "label": "Eagle 2",
      "organization": "NVIDIA,Nanjing University,Tsinghua University,Hong Kong Polytechnic University,Johns Hopkins University,New York University (NYU)",
      "domain": "Vision,Robotics,Language"
    },
    {
      "year": 2025.05,
      "logFlop": 24.54,
      "label": "DeepSeek-R1",
      "organization": "DeepSeek",
      "domain": "Language"
    },
    {
      "year": 2025.13,
      "logFlop": 26.54,
      "label": "Grok 3",
      "organization": "xAI",
      "domain": "Language,Vision,Multimodal"
    },
    {
      "year": 2025.15,
      "logFlop": 25.53,
      "label": "Claude 3.7 Sonnet",
      "organization": "Anthropic",
      "domain": "Language,Vision,Multimodal"
    },
    {
      "year": 2025.16,
      "logFlop": 26.58,
      "label": "GPT-4.5",
      "organization": "OpenAI",
      "domain": "Language,Vision,Multimodal"
    },
    {
      "year": 2025.18,
      "logFlop": 24.55,
      "label": "QwQ-32B",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.19,
      "logFlop": 24.73,
      "label": "Hunyuan-TurboS",
      "organization": "Tencent",
      "domain": "Language"
    },
    {
      "year": 2025.21,
      "logFlop": 24.1,
      "label": "EXAONE Deep 32B",
      "organization": "LG AI Research",
      "domain": "Language"
    },
    {
      "year": 2025.23,
      "logFlop": 24.52,
      "label": "DeepSeek-V3 (Mar 2025)",
      "organization": "DeepSeek",
      "domain": "Language"
    },
    {
      "year": 2025.26,
      "logFlop": 24.35,
      "label": "Llama 4 Maverick",
      "organization": "Meta AI",
      "domain": "Multimodal,Language,Vision"
    },
    {
      "year": 2025.26,
      "logFlop": 24.61,
      "label": "Llama 4 Scout",
      "organization": "Meta AI",
      "domain": "Multimodal,Language,Vision"
    },
    {
      "year": 2025.26,
      "logFlop": 25.71,
      "label": "Llama 4 Behemoth (preview)",
      "organization": "Meta AI",
      "domain": "Multimodal,Language,Vision"
    },
    {
      "year": 2025.27,
      "logFlop": 25.03,
      "label": "Pangu Ultra",
      "organization": "Huawei",
      "domain": "Language"
    },
    {
      "year": 2025.32,
      "logFlop": 24.68,
      "label": "Qwen3-235B-A22B",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.36,
      "logFlop": 24.14,
      "label": "Seed1.5-VL",
      "organization": "ByteDance",
      "domain": "Vision,Language,Multimodal,Video"
    },
    {
      "year": 2025.41,
      "logFlop": 24.6,
      "label": "DeepSeek-R1 (May 2025)",
      "organization": "DeepSeek",
      "domain": "Language"
    },
    {
      "year": 2025.45,
      "logFlop": 21.98,
      "label": "FGN",
      "organization": "Google DeepMind",
      "domain": "Earth science"
    },
    {
      "year": 2025.52,
      "logFlop": 26.7,
      "label": "Grok 4",
      "organization": "xAI",
      "domain": "Language,Multimodal,Vision"
    },
    {
      "year": 2025.53,
      "logFlop": 24.47,
      "label": "Kimi K2",
      "organization": "Moonshot",
      "domain": "Language"
    },
    {
      "year": 2025.54,
      "logFlop": 24.43,
      "label": "EXAONE 4.0 (32B)",
      "organization": "LG AI Research",
      "domain": "Language"
    },
    {
      "year": 2025.56,
      "logFlop": 24.2,
      "label": "Qwen3-Coder-480B-A35B",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.56,
      "logFlop": 24.68,
      "label": "Qwen3-235B-A22B-Thinking (Jul 2025)",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.56,
      "logFlop": 24.68,
      "label": "Qwen3-235B-A22B (Jul 2025)",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.59,
      "logFlop": 23.74,
      "label": "gpt-oss-20b",
      "organization": "OpenAI",
      "domain": "Language"
    },
    {
      "year": 2025.59,
      "logFlop": 24.65,
      "label": "GLM-4.5",
      "organization": "Z.ai (Zhipu AI),Tsinghua University",
      "domain": "Language"
    },
    {
      "year": 2025.59,
      "logFlop": 24.69,
      "label": "gpt-oss-120b",
      "organization": "OpenAI",
      "domain": "Language"
    },
    {
      "year": 2025.6,
      "logFlop": 25.82,
      "label": "GPT-5",
      "organization": "OpenAI",
      "domain": "Multimodal,Language,Vision"
    },
    {
      "year": 2025.67,
      "logFlop": 24.57,
      "label": "LongCat-Flash",
      "organization": "Meituan Inc",
      "domain": "Language"
    },
    {
      "year": 2025.68,
      "logFlop": 25.18,
      "label": "Qwen3-Max",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.71,
      "logFlop": 23.82,
      "label": "AgentFounder-30B",
      "organization": "Alibaba",
      "domain": "Language"
    },
    {
      "year": 2025.73,
      "logFlop": 22.56,
      "label": "Qwen3-Omni-30B-A3B",
      "organization": "Alibaba",
      "domain": "Multimodal,Language,Vision,Speech,Video"
    },
    {
      "year": 2025.74,
      "logFlop": 24.65,
      "label": "GLM-4.6",
      "organization": "Z.ai (Zhipu AI),Tsinghua University",
      "domain": "Language"
    },
    {
      "year": 2025.78,
      "logFlop": 24.78,
      "label": "Ling-1T",
      "organization": "Ant Group",
      "domain": "Language"
    },
    {
      "year": 2025.85,
      "logFlop": 24.62,
      "label": "Kimi K2 Thinking",
      "organization": "Moonshot",
      "domain": "Language"
    },
    {
      "year": 2025.89,
      "logFlop": 24.04,
      "label": "Olmo 3",
      "organization": "Allen Institute for AI",
      "domain": "Language"
    },
    {
      "year": 2025.96,
      "logFlop": 23.68,
      "label": "Nemotron 3-Nano-30B-A3B",
      "organization": "NVIDIA",
      "domain": "Language"
    },
    {
      "year": 2025.98,
      "logFlop": 24.65,
      "label": "GLM-4.7",
      "organization": "Z.ai (Zhipu AI)",
      "domain": "Language"
    },
    {
      "year": 2025.99,
      "logFlop": 24.18,
      "label": "K-EXAONE",
      "organization": "LG AI Research",
      "domain": "Language"
    },
    {
      "year": 2026.09,
      "logFlop": 24.76,
      "label": "Kimi K2.5",
      "organization": "Moonshot",
      "domain": "Language"
    },
    {
      "year": 2026.13,
      "logFlop": 24.84,
      "label": "GLM-5",
      "organization": "Z.ai (Zhipu AI)",
      "domain": "Language"
    }
  ],
  "frontier": [
    {
      "year": 1950.5,
      "logFlop": 1.6,
      "label": "Theseus",
      "organization": "Bell Laboratories",
      "domain": "Robotics",
      "description": "Claude Shannon’s electromechanical maze-solving mouse, widely considered the first artificial learning device. Using 90 telephone relays as memory, it could learn and remember the shortest path through a 25-square maze."
    },
    {
      "year": 1957.0,
      "logFlop": 5.84,
      "label": "Perceptron Mark I",
      "organization": "Cornell Aeronautical Laboratory,Cornell University",
      "domain": "Other",
      "description": "Frank Rosenblatt’s pioneering neural network, the first machine that could learn new skills by trial and error. Initially simulated on an IBM 704, the hardware version used 400 photosensitive units to model a simple retina."
    },
    {
      "year": 1959.09,
      "logFlop": 8.78,
      "label": "Pandemonium (morse)",
      "organization": "Massachusetts Institute of Technology (MIT)",
      "domain": "Language",
      "description": "Oliver Selfridge’s hierarchical pattern recognition model, one of the first computational approaches to pattern recognition. Its layered architecture of competing \"demons\" influenced the development of modern connectionist and AI models."
    },
    {
      "year": 1960.24,
      "logFlop": 8.86,
      "label": "Perceptron (1960)",
      "organization": "Cornell Aeronautical Laboratory",
      "domain": "Vision",
      "description": "An updated version of Rosenblatt’s perceptron applied to visual pattern recognition tasks. It demonstrated that a single-layer neural network could learn to classify simple visual inputs."
    },
    {
      "year": 1987.43,
      "logFlop": 10.44,
      "label": "NetTalk (dictionary)",
      "organization": "Princeton University",
      "domain": "Speech",
      "description": "Sejnowski and Rosenberg’s neural network that learned to pronounce English text aloud from a dictionary corpus. Trained via backpropagation, it demonstrated that neural networks could acquire complex linguistic rules without explicit programming."
    },
    {
      "year": 1987.43,
      "logFlop": 10.45,
      "label": "NetTalk (transcription)",
      "organization": "Princeton University",
      "domain": "Speech",
      "description": "A variant of NetTalk trained on phonetic transcriptions rather than a dictionary. It showed that neural networks could generalize pronunciation rules to unseen words, rivaling hand-crafted rule-based systems developed over years."
    },
    {
      "year": 1989.91,
      "logFlop": 11.26,
      "label": "Handwritten digit recognition network",
      "organization": "AT&T",
      "domain": "Vision",
      "description": "A back-propagation network by LeCun and colleagues at AT&T that recognized handwritten digits. Presented at NIPS 1989, it demonstrated the practical viability of neural networks for real-world pattern recognition."
    },
    {
      "year": 1989.92,
      "logFlop": 12.18,
      "label": "Zip CNN",
      "organization": "AT&T,Bell Laboratories",
      "domain": "Vision",
      "description": "Yann LeCun’s convolutional neural network trained to read handwritten zip codes for the U.S. Postal Service. The earliest real-world application of a CNN trained end-to-end with backpropagation, achieving 99% accuracy with 1% error rate."
    },
    {
      "year": 1992.33,
      "logFlop": 13.26,
      "label": "TD-Gammon",
      "organization": "IBM",
      "domain": "Games",
      "description": "Gerald Tesauro’s neural network that taught itself to play backgammon at near-championship level through self-play and temporal-difference learning. An early triumph of reinforcement learning, it discovered novel strategies that advanced human understanding of the game."
    },
    {
      "year": 1994.92,
      "logFlop": 13.27,
      "label": "Predictive Coding NN",
      "organization": "Technical University of Munich",
      "domain": "Language",
      "description": "Schmidhuber and Heil’s neural network that used predictive coding for text compression, outperforming Lempel-Ziv algorithms (used in gzip) on German newspaper text. An early demonstration that neural prediction could beat classical compression methods."
    },
    {
      "year": 1997.87,
      "logFlop": 13.5,
      "label": "LSTM",
      "organization": "Technical University of Munich",
      "domain": "Language",
      "description": "Hochreiter and Schmidhuber’s Long Short-Term Memory architecture, a breakthrough that solved the vanishing gradient problem in recurrent neural networks. Its gated memory cells could learn dependencies over 1,000+ time steps, becoming foundational for sequence modeling."
    },
    {
      "year": 2000.91,
      "logFlop": 13.71,
      "label": "PoE MNIST",
      "organization": "University College London (UCL)",
      "domain": "Vision",
      "description": "Geoffrey Hinton’s Products of Experts model applied to handwritten digit recognition on MNIST. It combined multiple simple probabilistic models multiplicatively, demonstrating an efficient approach to generative modeling of high-dimensional data."
    },
    {
      "year": 2000.91,
      "logFlop": 15.8,
      "label": "Neural LM",
      "organization": "University of Montreal / Université de Montréal",
      "domain": "Language",
      "description": "Yoshua Bengio’s neural probabilistic language model, which introduced the concept of learning distributed word representations (embeddings) jointly with a language model. A foundational paper that shaped all subsequent work on neural language modeling."
    },
    {
      "year": 2007.47,
      "logFlop": 17.89,
      "label": "KN-LM",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s modified Kneser-Ney language model trained on a trillion-word web corpus by Brants et al. It demonstrated that scaling n-gram models to unprecedented data sizes could significantly improve machine translation quality."
    },
    {
      "year": 2007.47,
      "logFlop": 18.16,
      "label": "SB-LM",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s Stupid Backoff language model, a simplified smoothing method designed for distributed computing at trillion-word scale. It traded statistical rigor for computational efficiency, enabling practical deployment of massive language models."
    },
    {
      "year": 2013.04,
      "logFlop": 18.42,
      "label": "DistBelief NNLM",
      "organization": "Google",
      "domain": "Language",
      "description": "A large-scale neural network language model trained on Google’s DistBelief distributed framework. It demonstrated that neural language models could be scaled to very large corpora, contributing to the development of word embedding techniques like Word2Vec."
    },
    {
      "year": 2014.46,
      "logFlop": 18.53,
      "label": "SPPNet",
      "organization": "Microsoft,Xi’an Jiaotong University,University of Science and Technology of China (USTC)",
      "domain": "Vision",
      "description": "Kaiming He’s Spatial Pyramid Pooling network, which eliminated the fixed-size input constraint of CNNs by pooling features at multiple scales. It was 24-102x faster than R-CNN and ranked second in object detection at ILSVRC 2014."
    },
    {
      "year": 2014.68,
      "logFlop": 19.04,
      "label": "VGG19",
      "organization": "University of Oxford",
      "domain": "Vision",
      "description": "A 19-layer deep convolutional network from Simonyan and Zisserman that showed depth was critical for visual recognition. Its simple architecture of stacked 3x3 convolutions became a widely used feature extractor across computer vision.",
      "date": "2014-09-04",
      "dateConfidence": "day",
      "dateSource": "https://en.wikipedia.org/wiki/VGG-19",
      "yearShifted": true,
      "originalYear": 2015
    },
    {
      "year": 2014.68,
      "logFlop": 19.09,
      "label": "VGG16",
      "organization": "University of Oxford",
      "domain": "Vision",
      "description": "The 16-layer variant of the VGGNet family, which achieved strong results on ImageNet with a uniform architecture of small convolution filters. It became one of the most popular pretrained models for transfer learning in computer vision.",
      "date": "2014-09-04",
      "dateConfidence": "day",
      "dateSource": "https://en.wikipedia.org/wiki/VGG-16",
      "yearShifted": true,
      "originalYear": 2015
    },
    {
      "year": 2014.69,
      "logFlop": 19.75,
      "label": "Seq2Seq LSTM",
      "organization": "Google",
      "domain": "Language",
      "description": "Sutskever, Vinyals, and Le’s sequence-to-sequence model using multilayer LSTMs for machine translation. This foundational architecture mapped variable-length input sequences to output sequences, enabling the neural machine translation revolution."
    },
    {
      "year": 2014.92,
      "logFlop": 20.47,
      "label": "SNM-skip",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s Sparse Non-negative Matrix language model with skip-gram features, evaluated on the One Billion Word Benchmark. It matched state-of-the-art recurrent neural network language models while offering superior computational scalability."
    },
    {
      "year": 2015.75,
      "logFlop": 20.58,
      "label": "AlphaGo Fan",
      "organization": "DeepMind",
      "domain": "Games",
      "description": "The first computer program to defeat a professional Go player on a full-sized board without handicap, beating European champion Fan Hui 5-0 in October 2015. It combined deep neural networks with Monte Carlo tree search."
    },
    {
      "year": 2016.07,
      "logFlop": 21.28,
      "label": "AlphaGo Lee",
      "organization": "DeepMind",
      "domain": "Games",
      "description": "The version of AlphaGo that defeated world champion Lee Sedol 4-1 in a historic match watched by over 200 million people. It marked a watershed moment for AI, conquering a game long considered a grand challenge.",
      "date": "2016-03-09",
      "dateConfidence": "day",
      "dateSource": "https://www.wikidata.org/wiki/Q23016184"
    },
    {
      "year": 2016.74,
      "logFlop": 21.82,
      "label": "GNMT",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s Neural Machine Translation system, an end-to-end deep LSTM with 8 encoder and 8 decoder layers plus attention. It reduced translation errors by 60% over phrase-based systems and powered Google Translate’s shift to neural methods."
    },
    {
      "year": 2018.33,
      "logFlop": 21.94,
      "label": "ResNeXt-101 32x48d",
      "organization": "Facebook",
      "domain": "Vision",
      "description": "Facebook’s 829-million-parameter image classifier pretrained on 940 million Instagram images with hashtag supervision. It achieved 85.4% top-1 accuracy on ImageNet, demonstrating the power of weakly supervised learning at billion-image scale."
    },
    {
      "year": 2019.71,
      "logFlop": 21.96,
      "label": "Megatron-LM (8.3B)",
      "organization": "NVIDIA",
      "domain": "Language",
      "description": "NVIDIA’s 8.3-billion-parameter transformer language model, 24x larger than BERT, trained using efficient model parallelism across 512 GPUs. It set state-of-the-art results on WikiText-103 and LAMBADA benchmarks."
    },
    {
      "year": 2019.71,
      "logFlop": 22.05,
      "label": "Megatron-LM (1.2B)",
      "organization": "NVIDIA",
      "domain": "Language",
      "description": "The 1.2-billion-parameter variant of NVIDIA’s Megatron-LM, demonstrating that efficient model-parallel training could scale transformer language models well beyond previous limits on GPU clusters."
    },
    {
      "year": 2019.71,
      "logFlop": 22.34,
      "label": "Megatron-BERT",
      "organization": "NVIDIA",
      "domain": "Language",
      "description": "NVIDIA’s 3.9-billion-parameter BERT model, 12x larger than BERT-Large, trained with the Megatron framework. It achieved state-of-the-art on the RACE reading comprehension benchmark, proving that scaling bidirectional models improves language understanding."
    },
    {
      "year": 2019.81,
      "logFlop": 22.52,
      "label": "T5-11B",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s 11-billion-parameter Text-to-Text Transfer Transformer, which reframed all NLP tasks as text generation. It achieved state-of-the-art on 18 of 24 benchmarks including GLUE, SuperGLUE, and SQuAD through a unified text-to-text framework."
    },
    {
      "year": 2019.82,
      "logFlop": 23.03,
      "label": "AlphaStar",
      "organization": "DeepMind",
      "domain": "Games",
      "description": "The first AI to reach Grandmaster level in StarCraft II, ranking in the top 0.2% of human players across all three races. It mastered a real-time strategy game requiring long-term planning, imperfect information, and rapid decision-making."
    },
    {
      "year": 2020.08,
      "logFlop": 23.05,
      "label": "Meena",
      "organization": "Google Brain",
      "domain": "Language",
      "description": "Google’s 2.6-billion-parameter open-domain chatbot trained on 341 GB of social media conversations using an Evolved Transformer architecture. It introduced the Sensibleness and Specificity Average metric and scored 23% higher than existing chatbots."
    },
    {
      "year": 2020.41,
      "logFlop": 23.5,
      "label": "GPT-3 175B (davinci)",
      "organization": "OpenAI",
      "domain": "Language",
      "description": "The first large language model to demonstrate emergent few-shot learning capabilities across diverse tasks without fine-tuning. With 175 billion parameters, it showed that scaling alone could unlock qualitatively new abilities in language models."
    },
    {
      "year": 2021.61,
      "logFlop": 23.57,
      "label": "Jurassic-1-Jumbo",
      "organization": "AI21 Labs",
      "domain": "Language",
      "description": "AI21 Labs’ 178-billion-parameter autoregressive language model with a uniquely large 250,000-token vocabulary including multi-word expressions. It was the largest model available to developers via API at its release."
    },
    {
      "year": 2021.67,
      "logFlop": 24.31,
      "label": "FLAN 137B",
      "organization": "Google Research",
      "domain": "Language",
      "description": "Google’s 137-billion-parameter model instruction-tuned on over 60 NLP tasks, demonstrating that fine-tuning on task instructions substantially improves zero-shot performance. It surpassed GPT-3’s zero-shot results on 20 of 25 evaluated benchmarks."
    },
    {
      "year": 2022.2,
      "logFlop": 24.41,
      "label": "GPT-3.5 (davinci-002)",
      "organization": "OpenAI",
      "domain": "Language",
      "description": "OpenAI’s instruction-tuned model combining code training with reinforcement learning from human feedback (RLHF). It served as the foundation for ChatGPT, which launched the mainstream AI chatbot era in November 2022."
    },
    {
      "year": 2022.49,
      "logFlop": 24.44,
      "label": "Minerva (540B)",
      "organization": "Google",
      "domain": "Language",
      "description": "Google’s 540-billion-parameter model fine-tuned on scientific papers and mathematical content from arXiv. It solved quantitative reasoning problems step-by-step without external tools, achieving state-of-the-art on MATH and GSM8k benchmarks."
    },
    {
      "year": 2023.2,
      "logFlop": 25.32,
      "label": "GPT-4 (Mar 2023)",
      "organization": "OpenAI",
      "domain": "Multimodal,Language,Vision",
      "description": "OpenAI’s multimodal model that accepts both text and image inputs, passing the bar exam in the 90th percentile and demonstrating broad expert-level performance across professional and academic benchmarks."
    },
    {
      "year": 2023.93,
      "logFlop": 25.7,
      "label": "Gemini 1.0 Ultra",
      "organization": "Google DeepMind",
      "domain": "Multimodal,Language,Vision",
      "description": "Google DeepMind’s most capable multimodal model at launch, natively understanding text, images, audio, and video. It was the first model to surpass human expert performance on the MMLU benchmark across 57 subjects.",
      "date": "2023-12-06",
      "dateConfidence": "day",
      "dateSource": "https://www.wikidata.org/wiki/Q124547173",
      "yearShifted": true,
      "originalYear": 2024
    },
    {
      "year": 2025.13,
      "logFlop": 26.54,
      "label": "Grok 3",
      "organization": "xAI",
      "domain": "Language,Vision,Multimodal",
      "description": "xAI’s frontier model trained on the 100,000-GPU Colossus supercluster with roughly 10x the compute of prior state-of-the-art models. It achieved 93.3% on AIME 2025 math competition problems using extended reasoning.",
      "date": "2024-05-15",
      "dateConfidence": "day",
      "dateSource": "https://en.wikipedia.org/wiki/Grok_3",
      "yearShifted": true,
      "originalYear": 2025
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