--- 格式版本: 2 标题: "Why we built the Responses API | OpenAI Developers" 原文链接: "https://developers.openai.com/blog/responses-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 08:36:15 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: "2026-09-06T18:27:26+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: "2026-09-06T10:29:11.495Z" 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: 32 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对Responses API的软件设计说明，涉及持久化推理、托管工具、多模态及TAUBench提升5%、缓存利用率提升40%—80%，不讨论超节点、AI机架或机架级硬件基础设施。来源为OpenAI官方开发者博客，正文完整；正文标注原始发布日期为2025年9月22日，与页面元数据日期冲突，应以前者为准。固定知识库未提供该API的历史对照，但未命中不能证明首次出现，且本文属于既有API的解释性文章，没有机架级新产品、标准、工程数据或部署信号，命中应用/API导向的弱相关否决。" AI质检模型: "gpt-5.6-sol" AI质检时间: "2026-09-08T16:34:58+08:00" AI主题相关性: 0 AI来源权威性: 15 AI新颖性: 5 AI技术细节: 2 AI商业部署信号: 0 AI完整性: 10 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/responses-api" --- Title: Why we built the Responses API | OpenAI Developers URL Source: https://developers.openai.com/blog/responses-api Published Time: Sun, 06 Sep 2026 08:36:15 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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API How the Responses API unlocks persistent reasoning, hosted tools, and multimodal workflows for GPT-5. Authors: Steve Coffey, Prashant Mital ![Image 2: Why we built the Responses API](https://developers.openai.com/images/blog/responses-api.png) With GPT-5 out in the world, we wanted to give some more context on the best way to integrate it, the [Responses API](https://platform.openai.com/docs/api-reference/responses), and why Responses is tailor-made for reasoning models and the agentic future. Every generation of OpenAI APIs has been built around the same question: _what’s the simplest, most powerful way for developers to talk to models?_ Our API design has always been guided by how the models themselves work. The very first `/v1/completions` endpoint was simple, but limiting: you gave the model a prompt, and it would simply finish your thought. Through techniques like few-shot prompting, developers could attempt to guide the model to do things like output JSON and answer questions, but these models were much less capable than what we are used to today. Then came RLHF, ChatGPT, and the post‑training era. Suddenly models weren’t just finishing your half‑written prose—they were _responding_ like a conversational partner. To keep up, we built `/v1/chat/completions` ([famously in a single weekend](https://x.com/athyuttamre/status/1899541474297180664)). By giving roles like `system`, `user`, `assistant`, we provided scaffolding to quickly build chat interfaces with custom instructions and context. Our models kept getting better. Soon, they began to see, hear, and speak. Function-calling in late 2023 turned out to be one of our most‑loved features. Around the same time we launched the Assistants API in beta: our first attempt at a fully agentic interface with hosted tools like code interpreter and file search. Some developers liked it, but it never achieved mass adoption due to an API design that was limiting and hard to adopt relative to Chat Completions. By late 2024 it was obvious we needed a unification: something as approachable as Chat Completions, as powerful as Assistants, but also purpose built for multimodal and reasoning models. Enter `/v1/responses`. ## `/v1/responses` is an agentic loop Chat Completions gave you a simple turn‑based chat interface. Responses instead gives you a structured loop for reasoning and acting. Think of it like working with a detective: you give them evidence, they investigate, they may consult experts (tools), and finally they report back. The detective keeps their private notes (reasoning state) between steps, but never hands them to the client. And here’s where reasoning models really shine: Responses preserves the model’s _reasoning state_ across those turns. In Chat Completions, reasoning is dropped between calls, like the detective forgetting the clues every time they leave the room. Responses keeps the notebook open; step‑by‑step thought processes actually survive into the next turn. That shows up in benchmarks (TAUBench +5%) and in more efficient cache utilization and latency. ![Image 3: responses vs chat completions](https://cdn.openai.com/devhub/tracks/diagram-responses-vs-cc.webp) Responses can also emit multiple output items: not just what the model _said_, but what it _did_. You get receipts—tool calls, structured outputs, intermediate steps. It’s like getting both the finished essay and the scratchpad math. Useful for debugging, auditing, and building richer UIs. ``` { "message": { "role": "assistant", "content": "I'm going to use the get_weather tool to find the weather.", "tool_calls": [ { "id": "call_88O3ElkW2RrSdRTNeeP1PZkm", "type": "function", "function": { "name": "get_weather", "arguments": "{\"location\":\"New York, NY\",\"unit\":\"f\"}" } } ], "refusal": null, "annotations": [] } } ``` Chat completions emits one **message** per request. The structure of a message is limiting: did the message or the function call come first? ``` { "id": "rs_6888f6d0606c819aa8205ecee386963f0e683233d39188e7", "type": "reasoning", "summary": [ { "type": "summary_text", "text": "**Determining weather response**\n\nI need to answer the user's question about the weather in San Francisco. ...." }, }, { "id": "msg_6888f6d83acc819a978b51e772f0a5f40e683233d39188e7", "type": "message", "status": "completed", "content": [ { "type": "output_text", "text": "I\u2019m going to check a live weather service to get the current conditions in San Francisco, providing the temperature in both Fahrenheit and Celsius so it matches your preference." } ], "role": "assistant" }, { "id": "fc_6888f6d86e28819aaaa1ba69cca766b70e683233d39188e7", "type": "function_call", "status": "completed", "arguments": "{\"location\":\"San Francisco, CA\",\"unit\":\"f\"}", "call_id": "call_XOnF4B9DvB8EJVB3JvWnGg83", "name": "get_weather" }, ``` Responses emits a list of **polymophic Items**. The ordering of actions the model took is clear. As a developer, you can choose which of these you want to display, log, or ignore entirely. ### Moving up the stack with hosted tools In the early days of function calling we noticed a key pattern: developers were using the model to both invoke APIs and also to search document stores to bring in external data sources–now known as RAG. But if you’re a developer just getting started, building a retrieval pipeline from scratch is a daunting and expensive endeavor. With Assistants, we introduced our first _hosted_ tools: `file_search` and `code_interpreter` , allowing the model to do RAG and write code to solve the problems you asked of it. In Responses, we’ve gone even further, adding web search, image gen, and MCP. And because tool execution happens server‑side through hosted tools like code interpreter or MCP, you’re not bouncing every call back through your own backend, ensuring better latency and round‑trip costs. ### Preserving reasoning safely So why go through all this trouble to obfuscate the model’s raw chain-of-thought (CoT)? Wouldn’t it be easier to just expose the CoT and let the clients treat them similar to other model outputs? The short answer is that exposing raw CoT has a number of risks: such as hallucinations, harmful content that wouldn’t be generated in a final response, and for OpenAI, opens up competitive risks. When we released o1-preview late last year, our Chief Scientist Jakub Pachocki wrote this in our blog: > We believe that a hidden chain of thought presents a unique opportunity for monitoring models. Assuming it is faithful and legible, the hidden chain of thought allows us to “read the mind” of the model and understand its thought process. For example, in the future we may wish to monitor the chain of thought for signs of manipulating the user. However, for this to work the model must have freedom to express its thoughts in unaltered form, so we cannot train any policy compliance or user preferences onto the chain of thought. We also do not want to make an unaligned chain of thought directly visible to users. Responses addresses this by: * Preserving reasoning internally, encrypted and hidden from the client. * Allowing safe continuation via `previous_response_id` or reasoning items, without exposing raw CoT. ## Why `/v1/responses` is the best way to build We designed Responses to be **stateful, multimodal, and efficient.** * **Agentic tool-use:** The Responses API makes it easy to supercharge agentic workflows with tools like File Search, Image Gen, Code Interpreter, and MCP. * **Stateful-by-default.** Conversations and tool state are tracked automatically. This makes reasoning and multi-turn workflows dramatically easier. GPT-5 integrated via Responses scores 5% better on TAUBench compared to Chat Completions, purely by taking advantage of preserved reasoning. * **Multimodal from the ground up.** Text, images, audio, function calls—all first-class citizens. We didn’t bolt modalities onto a text API; we designed the house with enough bedrooms from day one. * **Lower costs, better performance.** Internal benchmarks show 40–80% better cache utilization compared to Chat Completions. That means lower latency and lower costs. * **Better design:** We learned a lot from both the Chat Completions and Assistants APIs and made a number of small quality of life improvements in the ResponsesAPI and SDK, including * Semantic streaming events. * Internally-tagged polymorphism. * `output_text` helpers in the SDK (no more `choices.[0].message.content`). * Better organization of multimodal and reasoning params. ## What about Chat Completions? Chat Completions isn’t going away. If it works for you, keep using it. But if you want reasoning that persists, multimodal interactions that feel native, and an agentic loop that doesn’t require duct tape—Responses is the way forward. ## Looking ahead Just as Chat Completions replaced Completions, we expect Responses to become the default way developers build with OpenAI models. It’s simple when you need it to be, powerful when you want it to be, and flexible enough to handle whatever the next paradigm throws at us. This is the API we’ll be building on for the years ahead. Ask AI ## Docs agent Loading docs agent...