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Vapi Blog更新于 10月14日 12:28

Vapi 向所有客户开放 GPT-Live 测试版

GPT-Live is now in beta for every Vapi customer - Vapi AI Blog

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Vapi 宣布 GPT-Live 测试版向所有 Vapi 客户开放,此前仅限上月开始的限量测试与等待名单。GPT-Live 是 OpenAI 的全双工语音到语音模型,在 Vapi 上分为 speaker 和 reasoner 两个角色,speaker 全程保持通话并在需要查询或执行操作时把任务交给 reasoner,交接条件由提示词控制。迁移时需把原有单一系统提示词拆成 speaker 与 reasoner 两份,转写配置取消,语音改用 OpenAI 的 22 种音色,电话、工具、测试和可观测性等下游能力保持不变。

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Vapi 把 OpenAI 全双工语音模型拆成 speaker 与 reasoner 两个角色,读者可据此判断语音智能体的提示词与迁移方式会怎么变。

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Vapicon 2026 is coming

Last month we opened a limited beta for GPT-Live, OpenAI's full-duplex speech-to-speech model, and started a waitlist. Today the beta is open to every Vapi customer. If you have a Vapi account, you can build with GPT-Live now.

We spent the first month working hands-on with a small group of teams to get the model into production. That work shaped how GPT-Live runs on the platform. Now we're opening it up to learn from more of you.

What GPT-Live is

Voice agents have historically run on a modular pipeline: a speech-to-text model transcribes the call, an LLM reasons over the text, and a voice model speaks the response. GPT-Live is a speech-to-speech model. It listens to the caller's audio and generates its own speech, all in one model.

On Vapi, GPT-Live runs as two roles: a speaker and a reasoner. The speaker is the model itself. It handles the whole conversation and keeps listening while it talks. When the call needs a lookup or an action, the speaker delegates to the reasoner, a separate model that follows your procedures, runs your tools, and returns the result. You control that handoff in the prompt. The speaker never stops talking, so the conversation stays natural the entire time.

What changes on a call

It converses through the whole call without pausing to reason. The model backchannels, pauses naturally, and handles interruptions cleanly because it's listening the entire time it's talking. Callers can cut in or add a detail mid-response; With no handoffs between models, turn latency drops to something callers read as human; Tone and emphasis reach the model directly instead of being flattened into a transcript first; Stronger capture of numbers, names, and addresses, the details that decide verification and support calls; The reasoner handles multi-step procedures and complex tool calls while the speaker keeps talking

For use cases where sounding human decides the outcome, this is a step change.

How it runs on Vapi

GPT-Live runs on the same stack as your existing agents. Phone numbers, tools, tests, and observability all carry over.

What changes is how you set up the agent. A standard Vapi assistant has one system prompt that does everything: tone, procedures, tool rules, edge cases. GPT-Live splits that into two prompts, one for each model.

The speaker prompt shapes what the caller hears. Tone, pacing, how to greet, what to talk about while a tool runs. It also holds the delegation triggers, the specific conditions under which the speaker hands work to the reasoner. "Delegate when you know the service, location, and date." "Don't delegate if you can answer from what was already said."

The reasoner prompt holds the procedures. Tool rules, confirmation steps, what to check before taking an action, and how to format a result so the speaker can say it in a sentence.

In practice, migrating an assistant means pulling your existing prompt apart along that seam: conversation guidance goes to the speaker, procedures go to the reasoner. The transcriber config goes away since the model works from audio, and the voice switches to one of OpenAI's 22. Composer can do the first pass of that split from an existing assistant. If you're running a squad, each specialist assistant becomes a reasoner skill and the front-desk routing becomes delegation triggers, so the caller stays with one voice the whole time.

Everything downstream is unchanged. Function, API request, and MCP tools work as-is. Transcripts, recordings, scorecards, and Voice Simulations run against GPT-Live assistants the same way, so you can test the new version against the same calls before moving a phone number over.

How Vapi supports new models alongside modular pipelines

Vapi was built to be model-agnostic from day one. Assistants, tools, telephony, testing, and observability all sit independent of the model layer, so when a new model generation ships, it lands on the platform as a config change. You're never locked into a single provider.

That's what made the beta possible. Teams already running GPT-Live didn't rebuild anything to try it, and nothing is locked in if a different architecture serves the use case better.

New models are arriving faster than most platforms can adopt them. Our job is to make each generation production-ready as soon as it's viable, so you're choosing the best model for the job instead of inheriting whatever your platform is locked into.

Speech-to-speech is an addition to Vapi, not a replacement. You can run a modular pipeline or a speech-to-speech model, and choose per agent.

Modular pipelines give you maximum control: choose your transcription, intelligence, and voice models independently, tune each stage, and swap components as better ones ship. That control is why many production use cases are best served by modular configurations today, and why we'll keep investing in them. On Vapi, you run both side by side, and we're continuing to build infrastructure that tightens how the stages of a modular pipeline work together.

Start building

GPT-Live is live in beta for every Vapi customer today. Open the dashboard, create an assistant, pick GPT-Live as the model, and place a call. For a quickstart, specific examples and more details, read the docs and start building →

Table of Contents

Have your personal assistant make the call you keep putting off

Vapi for CX: own every layer, improve every week

Fast decisions alongside voice AI: early experiments with Jev

Campaigns, rebuilt: scale outbound calling with Vapi

Vapi simulations: test for every type of caller

Introducing Vapi Model Intelligence: model recommendations backed by production data

Introducing Vapi Monitoring

Vibe code voice agents

Your Voice Agents Need Tests. Now They Have Them.

Structured Outputs: Extract validated data from voice AI calls

Introducing Squads: Teams of Assistants

Real-time STT vs. Offline STT: Key Differences Explained

Introducing Vapi Workflows

Make your voice agents also chat with Vapi’s new Chat API

What Is ACD? An Introduction to Automatic Call Distribution

Tacotron 2 for Developers

Vapi x LiveKit Turn Detection

5 Days, 5 Features: Sneak Peek into the Future of Vapi

Introducing Vapi Voices

Vapi x Cartesia: Ultra-Realistic Voice AI with Sonic 2.0

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Vapi Blog