GPT-Live-1 for Developers (September 2026): Building Full-Duplex Voice Apps at $0.05 per Minute

GPT-Live-1 is OpenAI's full-duplex voice model for API developers who need conversations to behave more like natural calls than a sequence of recorded prompts and replies. At $0.05 per minute, it targets applications such as phone support, voice assistants, appointment scheduling, field-service workflows, and hands-free interfaces. The important change is not only lower-latency speech generation. Full-duplex communication changes how your application handles speaking, listening, interruptions, tools, state, and delegation to other models.

[Illustration space: full-duplex audio flow]

[Illustration space: GPT-Live-1 connected to Responses models and tools]

What “full-duplex” means

In a traditional turn-based voice system, the interaction usually follows a strict sequence: the user speaks, the client detects the end of speech, audio is sent to the model, the model generates a response, and the assistant speaks until the response is complete. Even a streaming Realtime system can still feel turn-based if the application waits for a clear end-of-turn signal before committing to an answer.

Full-duplex communication treats the user's audio and the assistant's audio as simultaneous streams. GPT-Live-1 can continue receiving incoming audio while it is generating or playing an answer. It can detect that the user has started speaking, update its interpretation of the conversation, and stop or revise its response without waiting for the previous speech output to finish.

This matters because human conversations contain overlap. People interrupt to correct details, answer a question before it is finished, say “wait,” or change the subject. A voice interface that cannot handle overlap forces users to wait through irrelevant audio or repeat themselves. Full-duplex behavior reduces that friction, but it also creates additional engineering responsibilities. Your client must manage audio playback cancellation, event ordering, partial transcripts, and the difference between speech that was generated and speech that was actually heard.

Interruption handling is an application concern

GPT-Live-1 may identify a barge-in event while it is speaking, but your application still needs to handle the audio lifecycle correctly. A typical client should:

Monitor microphone input continuously while assistant audio is playing.
Detect user speech using the model's events or a local voice-activity detector.
Immediately stop or fade the currently playing assistant audio.
Send an interruption or cancellation event to the session when required by the API.
Record how much assistant audio was actually played, rather than assuming the entire response was heard.
Preserve the user's new utterance as the next conversational input.

Do not implement interruption as only a user-interface action that pauses an audio element. If the server still considers the assistant response active, it may continue generating tokens, call a tool, or emit completion events after the client has stopped playback. The client and server session need a shared cancellation model.

There is also a state question. Suppose the assistant says, “I've cancelled your delivery,” and the user interrupts halfway through the sentence. If the cancellation tool has already completed, the business action may have happened even though the user did not hear the confirmation. If the tool has not run, the system should not mark the delivery as cancelled merely because the assistant began speaking. Store task state based on authoritative tool results, not on generated speech.

Using GPT-Live-1 as a voice front-end

GPT-Live-1 does not need to perform every reasoning task in your application. A strong architecture uses it as the low-latency voice front-end and delegates selected work to models in the Responses API. For example, you might use Luna for high-volume, routine tasks such as extracting an order number, classifying a request, summarizing a short account note, or drafting a simple response. Astra can handle complex reasoning such as multi-step troubleshooting, policy interpretation, exception handling, or planning across several tools.

There are two main delegation patterns.

Responses delegation

With Responses delegation, GPT-Live-1 asks your server to invoke a Responses model. The server receives a structured delegation request, selects the appropriate model, sends the relevant context to Responses, and returns a compact result to the live session.

This is usually the safer production pattern when you need centralized authorization, auditing, routing, cost controls, and access to server-only data. The live model can remain focused on conversation while your server decides whether a task belongs to Luna, Astra, or another internal service. Keep the delegated payload narrow. Send the user's request, the minimum relevant conversation state, and the tool or policy context needed for the task. Avoid forwarding an entire audio transcript when a structured intent and a few fields are sufficient.

Client delegation

With client delegation, the application running in the browser, mobile device, or desktop client invokes the Responses model directly after GPT-Live-1 identifies a task. This can reduce server orchestration for low-risk operations, but it exposes more routing logic to the client and makes authorization harder to enforce. It is appropriate only when the delegated operation is safe to expose, does not require privileged credentials, and can tolerate client-side failures or tampering.

Never rely on client delegation for destructive actions, account changes, payments, private records, or permissions. The server should validate those operations even if the client claims that GPT-Live-1 approved them.

Register tools once, define ownership clearly

Tools should have explicit names, descriptions, JSON schemas, authorization rules, and side-effect classifications. Registering a tool with GPT-Live-1 makes it available for voice interactions, but it does not replace server-side validation. Validate every argument, resolve the current user and account on the server, and ignore identifiers that the model is not allowed to choose.

Use tool_choice deliberately. An automatic setting is useful for ordinary conversations where the model can decide whether a tool is necessary. A required tool choice is useful when a particular workflow step must produce a structured lookup before the assistant continues. A forced “none” setting is useful when the user is asking for an explanation and no external action should occur.

For high-impact tools, separate preparation from execution. A voice assistant can prepare a refund, draft an appointment change, or assemble a purchase without committing it. Before execution, require a confirmation that is clear in both the transcript and the audio experience. Confirmation should name the action and important consequences: “This will cancel the appointment for Tuesday at 3 PM. Should I continue?” Do not treat ambiguous replies such as “okay” as sufficient if the preceding audio was interrupted or partially missed.

When to use Live, Agents API, or classic Realtime

Use GPT-Live-1 when the primary product requirement is natural, overlapping voice conversation with low perceived latency. It is a good fit for assistants that need to listen while speaking, handle barge-in, and move between short spoken turns without making the user manage push-to-talk controls.

Use the Agents API when the core requirement is a durable, tool-using agent rather than a voice transport. Agents are better suited to long-running tasks, multi-step plans, background execution, durable state, retries, approval steps, and workflows that may continue after the user disconnects. An Agents API workflow can still use GPT-Live-1 as its conversational interface, but the agent should own the durable task state.

Use classic Realtime when you need a more conventional streaming speech session, already have a turn-based design, need compatibility with an existing Realtime integration, or want direct control over audio events without adopting full-duplex behavior. It can be simpler for kiosk prompts, dictation, push-to-talk support, and short request-response experiences.

Pricing at $0.05 per minute

At $0.05 per minute, a ten-minute session costs $0.50 in model usage before other infrastructure charges. A thousand ten-minute sessions would therefore represent approximately $500 in GPT-Live-1 usage, excluding telephony, bandwidth, storage, transcription, delegated Responses calls, tool execution, and logging. Measure billed duration according to the API's accounting rules, especially if sessions remain open while users are silent.

Set maximum session durations, idle timeouts, concurrency limits, and per-user budgets. Track live minutes separately from Responses model usage because a voice session that delegates frequently can have two independent cost drivers. Log session identifiers, duration, interruptions, tool calls, delegation targets, and final task outcomes. Avoid storing raw audio by default unless it is required for debugging, compliance, or quality review.

Production checklist

Permissions: Authenticate the user, scope every tool to the user's account, and keep privileged API keys on the server.
Confirmations: Require explicit confirmation for purchases, cancellations, messages, account changes, and other irreversible actions.
Task state ownership: Store business state in your database or authoritative service. Do not infer completion from assistant audio.
Interruption recovery: Make tool calls idempotent and define what happens when the user interrupts before, during, or after execution.
Delegation controls: Route routine work to Luna and complex reasoning to Astra based on server-side policy, not only model instructions.
Input validation: Validate tool arguments, enforce limits, and reject missing, unexpected, or unauthorized fields.
Observability: Track latency to first audio, interruption rate, tool success rate, delegation cost, abandonment, and confirmed task completion.
Fallbacks: Provide text, keypad, or human-agent fallback paths when audio quality, permissions, or model confidence is insufficient.
Privacy: Clearly communicate recording and retention behavior, minimize transcript storage, and redact sensitive values from logs.

GPT-Live-1 is most valuable when treated as a conversational interface rather than the entire application. Full-duplex audio improves the human experience, while Responses delegation, server-owned tools, explicit confirmations, and durable task state keep the system reliable. Start with a narrow workflow, instrument interruptions and failed actions, and expand the assistant only after the underlying business operations are safe to repeat and recover.

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