How we built a realtime system for responsive voice AI in six months

For voice AI, knowing when to speak is harder than it sounds. Human speakers effortlessly hand off to each other in a fraction of a second, but previous voice AI systems couldn’t keep up with this rhythm. Their turn-based architecture relied on tiny models known as turn detectors, which faced an unenviable task: guess too soon, and the user gets cut off; guess too late, and the response feels sluggish. Only after the detector made its decision could the much larger LLM get to work.

GPT‑Live⁠, our third-generation voice system, removes the turn detector from the audio path. Its voice model is full-duplex, which means it can listen and speak at the same time. That eliminates the need for a separate detector and makes conversation feel more immediate and natural. When deeper reasoning or tool use is needed, GPT‑Live can also consult our frontier models, such as GPT‑5.5, without interrupting the flow of the conversation. Together, these capabilities give GPT‑Live an unprecedented combination of conversational responsiveness and intelligence.

Delivering this experience at scale required a new system architecture optimized for low latency. Unlike typical request-response inference, our system streams incoming audio into the voice model and outbound speech back to the user, while handling delegation on a separate asynchronous path. Over the last six months, we reworked model inference, context management, and media transport to keep speech flowing smoothly from end to end.

The architecture also creates a clean boundary between the core voice path and application logic. This makes it easy to customize application behavior without affecting responsiveness. This foundation powers a growing range of capabilities in ChatGPT Voice, including the newly launched ability to control your computer and coordinate your agents in the ChatGPT desktop app.