Hello GPT-4o

GPT‑4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time⁠(opens in a new window) in a 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.

Prior to GPT‑4o, you could use Voice Mode⁠ to talk to ChatGPT with latencies of 2.8 seconds (GPT‑3.5) and 5.4 seconds (GPT‑4) on average. To achieve this, Voice Mode is a pipeline of three separate models: one simple model transcribes audio to text, GPT‑3.5 or GPT‑4 takes in text and outputs text, and a third simple model converts that text back to audio. This process means that the main source of intelligence, GPT‑4, loses a lot of information—it can’t directly observe tone, multiple speakers, or background noises, and it can’t output laughter, singing, or express emotion.

With GPT‑4o, we trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. Because GPT‑4o is our first model combining all of these modalities, we are still just scratching the surface of exploring what the model can do and its limitations.

Explorations of capabilities

Model evaluations

As measured on traditional benchmarks, GPT‑4o achieves GPT‑4 Turbo-level performance on text, reasoning, and coding intelligence, while setting new high watermarks on multilingual, audio, and vision capabilities.

Language tokenization

These 20 languages were chosen as representative of the new tokenizer's compression across different language families

Preparedness Framework and in line with our voluntary commitments⁠. Our evaluations of cybersecurity, CBRN, persuasion, and model autonomy show that GPT‑4o does not score above Medium risk in any of these categories. This assessment involved running a suite of automated and human evaluations throughout the model training process. We tested both pre-safety-mitigation and post-safety-mitigation versions of the model, using custom fine-tuning and prompts, to better elicit model capabilities.external experts⁠ in domains such as social psychology, bias and fairness, and misinformation to identify risks that are introduced or amplified by the newly added modalities. We used these learnings to build out our safety interventions in order to improve the safety of interacting with GPT‑4o. We will continue to mitigate new risks as they’re discovered.

Cybersecurity