ChatGPT’s voice mode now listens and speaks at the same time, passing harder questions posed to the conversational model to a reasoning model in the background.
What's new: On July 8, OpenAI released a pair of voice models — GPT-Live-1 and GPT-Live-1 mini — that power a rebuilt ChatGPT Voice. Both models process incoming and outgoing audio at once instead of waiting for a turn to end. Telecom engineers call this full-duplex: transmission and reception flow at the same time, like a telephone call, rather than alternating, like a walkie-talkie. When a response requires deeper thinking, the voice model hands the question to GPT-5.5 and keeps talking while that model works. The system replaces Advanced Voice Mode (AVM), a single model that listened, reasoned, and spoke in discrete turns.
- Input/output: Both models are speech in, speech out, processed continuously so input and output overlap. In ChatGPT, conversations are accompanied by on-screen visual cards (weather, stocks, sports, maps) rendered by the app. The app accepts images and file uploads. Live video and screen sharing are absent. OpenAI says it is working to add both, and they remain available in the legacy Standard and AVM.
- Features: Live translation; nine remastered voices, all predefined, with safeguards that block mimicking real people's voices; user-selectable reasoning effort on GPT-Live-1 (Instant, Medium, High) — Instant runs GPT-5.5 Instant in the background while Medium and High run GPT-5.5 Thinking at matching effort. GPT-Live-1 mini only calls GPT-5.5 Instant.
- Performance: In OpenAI's tests, GPT-Live-1 at high reasoning scored 84.2 percent on GPQA versus 45.3 percent for its predecessor, AVM; human raters preferred GPT-Live-1 to AVM 75.7 percent of the time and GPT-Live-1 mini 69.2 percent.
- Availability: Available now on iOS, Android, and ChatGPT.com globally. GPT-Live-1 is the default for Go, Plus, and Pro plans for no extra charge; GPT-Live-1 mini for the free plan. No developer API has shipped; for now, OpenAI's developer voice option remains GPT-Realtime-2, which reached the Realtime API in May.
- Undisclosed: Parameter counts, architecture details, training data, knowledge cutoff, latency measurements, usage-based pricing
How it works: OpenAI published a GPT-Live system card that describes a system of several models working as an ensemble. The voice models differ from their predecessors in two ways: they process audio continuously rather than turn-by-turn, and they hand deeper work to a separate model.
- Each GPT-Live model processes incoming audio while producing its own output, deciding its next action many times each second. These actions include talk, listen, wait, break in, backchannel (producing “hmm,” “yeah,” and similar signs of listening), or trigger a tool.
- When a question calls for web search, deeper reasoning, or multi-step work with tools (looking up information and acting on it across several turns), the voice model hands the task to GPT-5.5, keeps talking while that model runs, and weaves the result back in. The two models share the conversation’s context but are orchestrated and served separately. OpenAI says it will point GPT-Live at newer reasoning models as they ship.
- Safety checks run alongside the conversation. The system inspects inputs and outputs as they unfold and can steer or interrupt a reply, play a spoken safety message, put support resources on screen as text or voice, or end the conversation in higher-risk cases.
Performance: In OpenAI’s evaluations, the biggest gains show up on the tasks routed to GPT-5.5, with the delegation system working as designed. Every comparison OpenAI published pits GPT-Live against its own predecessor, AVM, rather than rival voice models, and its strongest claims rest on internal benchmarks.
- On GPQA (graduate-level science across biology, chemistry, and physics), GPT-Live-1 at high reasoning scored 84.2 percent against 45.3 percent for AVM.
- The gap is sharper on BrowseComp (agentic web search for hard-to-find facts): GPT-Live-1 at high reasoning answered 75.2 percent of questions correctly against 0.7 percent for AVM.
- Human raters preferred GPT-Live-1’s conversational quality to that of AVM 75.7 percent of the time and GPT-Live-1 mini 69.2 percent, in matched 5-to-10-minute conversations that ranked overall preference, turn-taking, interruptions, and conversational flow.
- Both GPT-Live models outperformed AVM at flagging disallowed content, with notable gains on illicit behavior (97 percent for GPT-Live-1 versus 74 percent for AVM) and self-harm (96 percent versus 89 percent). The gains were sharper on adversarial prompts: GPT-Live-1 flagged 84 percent of mental health-related queries versus AVM’s 57 percent, and 98 percent for self-harm versus AVM’s 72 percent.
Behind the news: Both halves of GPT-Live’s design (full-duplex processing and reasoning-model orchestration) have precedents. Alibaba’s Qwen2.5-Omni Thinker-Talker architecture trained a text-generating “thinker” and a speech “talker” as one system. Thinking Machines Lab paired a foreground interaction model with a background reasoner in TML-Interaction-Small. Kyutai’s Moshi, billed by its authors as the “first real-time full-duplex spoken large language model,” arrived in 2024; Nvidia released PersonaPlex, an open-weights model built on Moshi, in January; and Google’s Gemini Live currently offers continuous conversation along with camera and screen sharing. OpenAI can boast that it ships the combination to a mass audience; the company says more than 150 million people use ChatGPT’s voice and dictation features each week.
Why it matters: OpenAI’s investment in voice systems may signal broader ambitions. The company plans to unveil a portable, screenless smart speaker that relies entirely on GPT-Live voice interactions before the end of this year, with devices available in early 2027, according to Bloomberg. The device will reportedly learn more about its user over time, allowing for highly personalized interactions, and tap into more powerful AI models than smart speakers currently on the market. OpenAI’s strategy hinges on whether it can successfully make voice a first-class interface for everyday productivity.
We're thinking: Anyone who has shipped voice systems has spent years fighting turn detection from the outside: tuning silence thresholds, padding timers, guessing whether a pause means the speaker is done or just thinking. Full-duplex doesn't make that guess better; it makes the guess redundant, because the model decides moment to moment whether to speak, wait, or just offer a "mhmm." Still, the less flashy design choice here might matter more in the long run. Because the conversational layer is decoupled from the reasoning layer, GPT-Live inherits every frontier model improvement, and it never has to pick between responding quickly and thinking hard. Full-duplex makes voice pleasant to talk to; delegation makes it worth talking to.