GPT-4o System Card
GPT-4o is an autoregressive omni model that accepts as input any combination
of text, audio, image, and video, and generates any combination of text, audio,
and image outputs. It's trained end-to-end across text, vision, and audio,
meaning all inputs and outputs are processed by the same neural network. GPT-4o
can respond to audio inputs in as little as 232 milliseconds, with an average
of 320 milliseconds, which is similar to human response time in conversation.
It matches GPT-4 Turbo performance on text in English and code, with
significant improvement on text in non-English languages, while also being much
faster and 50\% cheaper in the API. GPT-4o is especially better at vision and
audio understanding compared to existing models. In line with our commitment to
building AI safely and consistent with our voluntary commitments to the White
House, we are sharing the GPT-4o System Card, which includes our Preparedness
Framework evaluations. In this System Card, we provide a detailed look at
GPT-4o's capabilities, limitations, and safety evaluations across multiple
categories, focusing on speech-to-speech while also evaluating text and image
capabilities, and measures we've implemented to ensure the model is safe and
aligned. We also include third-party assessments on dangerous capabilities, as
well as discussion of potential societal impacts of GPT-4o's text and vision
capabilities.
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Rajan Troll
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Randall Lin
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Rapha Gontijo Lopes
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Raul Puri
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Reah Miyara
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Reimar Leike
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Renaud Gaubert
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Reza Zamani
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Ricky Wang
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Rob Donnelly
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Rob Honsby
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Rocky Smith
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Rohan Sahai
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Rohit Ramchandani
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Romain Huet
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Rory Carmichael
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Rowan Zellers
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Roy Chen
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Ruby Chen
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Ruslan Nigmatullin
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Ryan Cheu
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Saachi Jain
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Sam Altman
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Sam Schoenholz
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Sam Toizer
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Samuel Miserendino
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Sandhini Agarwal
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Sara Culver
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Scott Ethersmith
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Scott Gray
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Sean Grove
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Sean Metzger
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Shamez Hermani
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Shantanu Jain
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Shengjia Zhao
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Sherwin Wu
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Shino Jomoto
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Shirong Wu
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Shuaiqi
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Xia
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Sonia Phene
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Spencer Papay
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Srinivas Narayanan
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Steve Coffey
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Steve Lee
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Stewart Hall
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Suchir Balaji
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Tal Broda
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Tal Stramer
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Tao Xu
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Tarun Gogineni
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Taya Christianson
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Ted Sanders
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Tejal Patwardhan
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Thomas Cunninghman
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Thomas Degry
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Thomas Dimson
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Thomas Raoux
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Thomas Shadwell
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Tianhao Zheng
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Todd Underwood
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Todor Markov
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Toki Sherbakov
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Tom Rubin
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Tom Stasi
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Tomer Kaftan
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Tristan Heywood
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Troy Peterson
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Tyce Walters
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Tyna Eloundou
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Valerie Qi
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Veit Moeller
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Vinnie Monaco
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Vishal Kuo
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Vlad Fomenko
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Wayne Chang
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Weiyi Zheng
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Wenda Zhou
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Wesam Manassra
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Will Sheu
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Wojciech Zaremba
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Yash Patil
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Yilei Qian
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Yongjik Kim
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Youlong Cheng
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Yu Zhang
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Yuchen He
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Yuchen Zhang
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Yujia Jin
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Yunxing Dai
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Yury Malkov
GPT-4o System Card
GPT-4o is an autoregressive omni model that accepts as input any combination
of text, audio, image, and video, and generates any combination of text, audio,
and image outputs. It's trained end-to-end across text, vision, and audio,
meaning all inputs and outputs are processed by the same neural network. GPT-4o
can respond to audio inputs in as little as 232 milliseconds, with an average
of 320 milliseconds, which is similar to human response time in conversation.
It matches GPT-4 Turbo performance on text in English and code, with
significant improvement on text in non-English languages, while also being much
faster and 50\% cheaper in the API. GPT-4o is especially better at vision and
audio understanding compared to existing models. In line with our commitment to
building AI safely and consistent with our voluntary commitments to the White
House, we are sharing the GPT-4o System Card, which includes our Preparedness
Framework evaluations. In this System Card, we provide a detailed look at
GPT-4o's capabilities, limitations, and safety evaluations across multiple
categories, focusing on speech-to-speech while also evaluating text and image
capabilities, and measures we've implemented to ensure the model is safe and
aligned. We also include third-party assessments on dangerous capabilities, as
well as discussion of potential societal impacts of GPT-4o's text and vision
capabilities.
GPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.
autoregressive model omnimodal end-to-end training vision audio speech-to-speech safety evaluations