--- 格式版本: 2 标题: "Developer notes on the Realtime API | OpenAI Developers" 原文链接: "https://developers.openai.com/blog/realtime-api" 发布日期: "2026-09-06" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:local:strict_original_body" 发布时间证据: "Published Time: Sun, 06 Sep 2026 04:59:33 GMT" 发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body" 发布时间校准置信度: "high" 发布时间候选数量: 4 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-09-06T18:27:30+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-09-06T17:44:27+08:00" 入库时间: "2026-09-06T10:29:14.366Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://developers.openai.com/blog" 匹配关键词: - "deployment" - "performance" - "latency" - "throughput" - "AI" 相关厂家: - "OpenAI" - "Microsoft" - "AWS" - "Google" - "Oracle" 相关专家: [] 内容类型: "网页" 抓取工具: "Jina Reader" 清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 35 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文主线是OpenAI Realtime API及语音应用开发，介绍GA接口、异步函数调用、上下文截断、SIP/WebRTC侧带连接等功能，不涉及超节点、AI机柜或机架级基础设施。来源为OpenAI官方开发者博客，正文实际日期为2025-09-12，与页面元数据日期冲突，应以正文为准。固定知识库未提供该API事件的历史记录，但不能据此认定首次出现；新增内容即使可核验，也属于应用API而非机架级技术。页面正文完整且可追溯，但无机架产品、关键部件、客户部署或量产信号，命中弱相关及应用导向否决项。" AI质检模型: "gpt-5.6-sol" AI质检时间: "2026-09-08T16:34:54+08:00" AI主题相关性: 0 AI来源权威性: 15 AI新颖性: 7 AI技术细节: 2 AI商业部署信号: 2 AI完整性: 9 AI评分提示词版本: "v17-精简生产版" AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d" AI评分知识库版本: "knowledge_base_v1-20260819+runtime.88" AI评分知识库SHA256: "187ddf9763173d03e5c13ef1e1afe58e86093a26ab37f0e4d189c8eb19dacf5b" AI评分知识库检索词: "[\"OpenAI\",\"Microsoft\",\"AWS\",\"Google\",\"Oracle\",\"deployment\",\"performance\",\"latency\",\"throughput\",\"https://developers.openai.com/blog\",\"RAS\",\"NPU\"]" AI评分知识库命中: "[{\"id\":\"july-correct-0018\",\"title\":\"AMD, Cerebras partner on joint Helios rack-scale AI inference platform\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Microsoft\",\"Oracle\",\"performance\",\"latency\",\"throughput\",\"RAS\"],\"rank\":-20.408108891439618},{\"id\":\"july-correct-0033\",\"title\":\"Microsoft, Alphabet, Meta Pivot from Buy to Build in AI\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Microsoft\",\"AWS\",\"Google\",\"deployment\",\"performance\",\"RAS\"],\"rank\":-18.845160932854792},{\"id\":\"july-correct-0080\",\"title\":\"AMD and Cerebras Announce Industry-Leading Ultra-Low-Latency and High Throughput AI Inference Solution\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Microsoft\",\"AWS\",\"deployment\",\"performance\",\"latency\",\"throughput\",\"RAS\"],\"rank\":-18.001565535425346},{\"id\":\"runtime-15c79e6868a9b43f99c061a9\",\"title\":\"Nvidia’s AI Boom Tests Data Center Infrastructure Limits\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-27\",\"matchedTerms\":[\"OpenAI\",\"AWS\",\"Oracle\",\"deployment\",\"performance\",\"RAS\"],\"rank\":-15.23727524593058},{\"id\":\"runtime-4abbccc42d96af674efc7768\",\"title\":\"OpenAI’ Jalapeño: Better Than Nvidia Blackwell\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-25\",\"matchedTerms\":[\"OpenAI\",\"Microsoft\",\"AWS\",\"Google\",\"deployment\",\"performance\",\"latency\",\"throughput\",\"RAS\",\"NPU\"],\"rank\":-14.196371038268706}]" 采集批次: "2026年9月6日17点41分46秒" 采集批次ID: "20260906-174146-0dbc83f7" 去重键: "https://developers.openai.com/blog/realtime-api" --- Title: Developer notes on the Realtime API | OpenAI Developers URL Source: https://developers.openai.com/blog/realtime-api Published Time: Sun, 06 Sep 2026 04:59:33 GMT Markdown Content: For the complete documentation index, see [llms.txt](https://developers.openai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to the page URL. 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Realtime API Details worth noticing in recent realtime speech-to-speech updates Author: Peter Bakkum ![Image 2: Developer notes on the Realtime API](https://developers.openai.com/images/blog/agent-online.png) We recently [announced](https://openai.com/index/introducing-gpt-realtime/) our latest speech-to-speech model, `gpt-realtime`, in addition to the general availability of the Realtime API and a bunch of new API features. The Realtime API and speech-to-speech (s2s) model graduated to general availability (GA) with major improvements in model quality, reliability, and developer ergonomics. While you can discover the new API features in [the docs](https://platform.openai.com/docs/guides/realtime) and [API reference](https://platform.openai.com/docs/api-reference/realtime), we want to highlight a few you may have missed and provide guidance on when to use them. If you’re integrating with the Realtime API, we hope you’ll find these notes interesting. ## Model improvements The new model includes a number of improvements meant to better support production voice apps. We’re focusing on API changes in this post. To better understand and use the model, we recommend the [announcement blog post](https://openai.com/index/introducing-gpt-realtime/) and [realtime prompting guide](https://developers.openai.com/cookbook/examples/realtime_prompting_guide). However, we’ll point out some specifics. A few key pieces of advice for using this model: * Experiment with prompting in the [realtime playground](https://platform.openai.com/playground/realtime). * Use the `marin` or `cedar` voices for best assistant voice quality. * Rewrite prompts for the new model. Due to instruction-following improvements, specific instructions are now much more powerful. * For example, a prompt that said, “Always say X when Y,” may have been treated by the old model as vague guidance, whereas the new the model may adhere to it in unexpected situations. * Pay attention to the specific instructions you’re providing. Assume instructions will be followed. ## API shape changes We updated the Realtime API shape with the GA launch, meaning there’s a beta interface and a GA interface. We recommend that clients migrate to integrate against the GA interface, as it gives new features, and the beta interface will eventually be deprecated. A complete list of the changes needed for migration can be found in the [beta to GA migration docs](https://platform.openai.com/docs/guides/realtime#beta-to-ga-migration). You can access the new `gpt-realtime` model with the beta interface, but certain features may be unsupported. See below for more details. ### Feature availability The Realtime API GA release includes a number of new features. Some of these are enabled on older models, and some are not. | Feature | GA model | Beta model | | --- | --- | --- | | Image input | ✅ | ❌ | | Long context | ✅ | ✅ | | Async function calling | ✅ | ❌ | | Prompts | ✅ | ✅ | | MCP | ✅ _Best with async FC_ | ✅ _Limited without async FC*_ | | Audio token → text | ✅ | ❌ | | EU data residency | ✅ | ✅ _06-03 only_ | | SIP | ✅ | ✅ | | Idle timeouts | ✅ | ✅ | *Because the beta model lacks async function calling, pending MCP tool calls without an output may not be treated well by the model. We recommend using the GA model with MCP. ### Changes to temperature The GA interface has removed `temperature` as a model parameter, and the beta interface limits temperature to a range of `0.6 - 1.2` with a default of `0.8`. You may be asking, “Why can’t users set temperature arbitrarily and use it for things like making the response more deterministic?” The answer is that temperature behaves differently for this model architecture, and users are nearly always best served by setting temperature to the recommended `0.8`. From what we’ve observed, there isn’t a way to make these audio responses deterministic with low temperatures, and higher temperatures result in audio abberations. We recommend experimenting with prompting to control these dimensions of model behavior. ## New features In addition to the changes from beta to GA, we’ve added several new features to the Realtime API. All features are covered in [the docs](https://platform.openai.com/docs/guides/realtime) and [API reference](https://platform.openai.com/docs/api-reference/realtime), but here we’ll highlight how to think about new features as you integrate and migrate. ### Conversation idle timeouts For some applications, it’d be unexpected to have a long gap of input from the user. Imagine a phone call—if we didn’t hear from the person on the other line, we’d ask about their status. Maybe the model missed what the user said, or maybe the user isn’t sure if the model is still speaking. We’ve added a feature to automatically trigger the model to say something like: “Are you still there?” Enable this feature by setting `idle_timeout_ms` on the `server_vad` settings for turn detection. The timeout value will be applied after the last model response’s audio has finished playing— i.e., timeout value is set to the `response.done` time plus audio playback duration plus timeout time. If VAD does not fire for that period, the timeout is triggered. When the timeout is triggered, the server sends an [`input_audio_buffer.timeout_triggered`](https://platform.openai.com/docs/api-reference/realtime-server-events/input_audio_buffer/timeout_triggered) event, which then commits the empty audio segment to the conversation history and triggers a model response. Committing the empty audio gives the model a chance to check whether VAD failed and there was a user utterance during the relevant period. Clients can enable this feature like so: ``` { "type": "session.update", "session": { "type": "realtime", "instructions": "You are a helpful assistant.", "audio": { "input": { "turn_detection": { "type": "server_vad", "idle_timeout_ms": 6000 } } } } } ``` ### Long conversations and context handling We’ve tweaked how the Realtime API handles long sessions. A few things to keep in mind: * Realtime sessions can now last up to 60 minutes, up from 30 minutes. * The `gpt-realtime` model has a token window of 32,768 tokens. Responses can consume a maximum of 4,096 tokens. This means the model has a maximum input of 28,672 tokens. * The session instructions plus tools can have a maximum length of 16,384 tokens. * The service will automatically truncate (drop) messages when the session reaches 28,672 tokens, but this is configurable. * The GA service will automatically drop some audio tokens when a transcript is available to save tokens. #### Configuring truncation settings What happens when the conversation context window fills up to the token limit is that after the limit is reached, the Realtime API automatically starts truncating (dropping) messages from the beginning of the session (the oldest messages). You can disable this truncation behavior by setting `"truncation": "disabled"`, which instead throws an error when a response has too many input tokens. Truncation is useful, however, because the session continues even if the input size grows too large for the model. The Realtime API doesn’t do summarization or compaction of dropped messages, but you can implement it on your own. A negative effect of truncation is that changing messages at the beginning of the conversation busts the [token prompt cache](https://platform.openai.com/docs/guides/prompt-caching). Prompt caching works by identifying identical, exact-match content prefixing your prompts. On each subsequent turn, only the tokens that haven’t changed are cached. When truncation alters the beginning of the conversation, it reduces the number of tokens that can be cached. We’ve implemented a feature to mitigate this negative effect by truncating more than necessary whenever truncation occurs. Set retention ratio to `0.8` to truncate 20% of the context window rather than truncating just enough to keep the input token count under the ceiling. The idea is to truncate _more_ of the context window _once_, rather than truncating a little bit every time, so you bust the cache less often. This cache-friendly approach can keep costs down for long sessions that reach input limits. ``` { "type": "session.update", "session": { "truncation": { "type": "retention_ratio", "retention_ratio": 0.8 } } } ``` ### Asynchronous function calling Whereas the Responses API forces a function response immediately after the function call, the Realtime API allows clients to continue a session while a function call is pending. This continuation is good for UX, allowing realtime conversations to continue naturally, but the model sometimes hallucinates the content of a nonexistent function response. To mitigate this issue, the GA Responses API adds placeholder responses with content we’ve evaluated and tuned in experiments to ensure the model performs gracefully, even while awaiting a function response. If you ask the model for the results of a function call, it’ll say something like, “I’m still waiting on that.” This feature is automatically enabled for new models—no changes necessary on your end. ### EU data residency EU data residency is now supported specifically for the `gpt-realtime-2025-08-28` and `gpt-4o-realtime-preview-2025-06-03`. Data residency must be explicitly enabled for an organization and accessed through `https://eu.api.openai.com`. ### Tracing The Realtime API logs traces to the [developer console](https://platform.openai.com/logs?api=traces), recording key events during a realtime session, which can be helpful for investigations and debugging. As part of GA, we launched a few new event types: * Session updated (when `session.updated` events are sent to the client) * Output text generation (for text generated by the model) ### Hosted prompts You can now use [prompts with the Realtime API](https://platform.openai.com/docs/guides/realtime-models-prompting#update-your-session-to-use-a-prompt) as a convenient way to have your application code refer to a prompt that can be edited separately. Prompts include both instructions and session configuration, such as turn detection settings. You can create a prompt in the [realtime playground](https://platform.openai.com/audio/realtime), iterating on it and versioning it as needed, and then a client can reference that prompt by ID, like so: ``` { "type": "session.update", "session": { "type": "realtime", "prompt": { "id": "pmpt_123", // your stored prompt ID "version": "89", // optional: pin a specific version "variables": { "city": "Paris" // example variable used by your prompt } }, // You can still set direct session fields; these override prompt fields if they overlap: "instructions": "Speak clearly and briefly. Confirm understanding before taking actions." } } ``` If a prompt setting overlaps with other configuration passed to the session, as in the example above, the session configuration takes precedence, so a client can either use the prompt’s config or manipulate it at session time. ### Sideband connections The Realtime API allows clients to connect directly to the API server via WebRTC or SIP. However, you’ll most likely want tool use and other business logic to reside on your application server to keep this logic private and client-agnostic. Keep tool use, business logic, and other details secure on the server side by connecting over a sideband control channel. We now have sideband options for both SIP and WebRTC connections. A sideband connection means there are two active connections to the same realtime session: one from the user’s client and one from your application server. The server connection can be used to monitor the session, update instructions, and respond to tool calls. For more information, see [documentation for sideband connections](https://platform.openai.com/docs/guides/realtime-server-controls). ## Start building We hope this was a helpful way to understand what’s changed with the generally available Realtime API and new realtime models. Now that you have the updated framing, [see the realtime docs](https://platform.openai.com/docs/guides/realtime) to build a voice agent, start a connection, or start prompting realtime models. Ask AI ## Docs agent Loading docs agent...