title: GPT-4o System Card
summary: 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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paper_authors.name: Leo Chen
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paper_authors.name: Li Jing
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paper_authors.name: Lia Guy
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paper_authors.name: Liam Fedus
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paper_authors.name: Liang Zhou
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paper_authors.name: Lien Mamitsuka
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paper_authors.name: Lyric Doshi
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paper_authors.name: Suchir Balaji
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paper_authors.name: Tal Broda
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paper_authors.name: Tal Stramer
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paper_authors.name: Tao Xu
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paper_authors.name: Tarun Gogineni
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paper_authors.name: Taya Christianson
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paper_authors.name: Ted Sanders
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paper_authors.name: Todor Markov
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paper_authors.name: Toki Sherbakov
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paper_authors.name: Tom Rubin
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paper_authors.name: Weiyi Zheng
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paper_authors.name: Wenda Zhou
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paper_authors.name: Wesam Manassra
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paper_authors.name: Will Sheu
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paper_authors.name: Wojciech Zaremba
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paper_authors.name: Yash Patil
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paper_authors.name: Yilei Qian
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paper_authors.name: Yongjik Kim
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paper_authors.name: Youlong Cheng
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paper_authors.name: Yu Zhang
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paper_authors.name: Yuchen He
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paper_authors.name: Yuchen Zhang
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paper_authors.name: Yujia Jin
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paper_title: GPT-4o System Card
paper_summary: 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.
paper_ai_keywords: autoregressive omni model end-to-end training neural network speech-to-speech safety evaluations third-party assessments