> ## Documentation Index > Fetch the complete documentation index at: https://platform.stepfun.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Responses API Call the Responses API to get the model-generated response data. This endpoint is compatible with the OpenAI Responses API request and response format. ## Endpoint `POST https://api.stepfun.ai/v1/responses` ## Request Parameters * `model` `string` ***required***
Name of the model to use. Currently only `step-3.7-flash` is supported. * `input` `string or object array` ***required***
Input content. Either a plain text string, or an ordered array of messages / events. * Plain text input `string`
Equivalent to a single text message with `role=user`. * Message / event array `object array`
Each element may be one of the following types: * Text message `object` * `role` `string`
Message role. Valid values: `user`, `assistant`, `system`. * `content` `string or object array`
Message content. Either a plain text string, or an array of content blocks for multimodal input. * Plain text content `string` * Content block array `object array` * Text block `object` * `type` `string`
Always `input_text`. * `text` `string`
Text content. * Image block `object` * `type` `string`
Always `input_image`. * `image_url` `string or object`
Image source. Either a string, or an object `{ "url": "...", "detail": "high" }`. Base64 data URLs (format `data:image/jpeg;base64,${base64_string}`) are recommended; otherwise the external URL must be publicly reachable from the server. * Video block `object` * `type` `string`
Always `input_video`. * `video_url` `string or object`
Video source. Either a string, or an object `{ "url": "...", "detail": "low" }`. The video URL must be publicly reachable from the server. * Function call `object` (returned by the model in a previous turn; must be passed back verbatim in multi-turn conversations) * `type` `string`
Always `function_call`. * `id` `string`
Unique ID for the function call, generated by the server. * `call_id` `string`
Correlation ID used to pair with the subsequent `function_call_output`. * `name` `string`
Tool name. * `arguments` `string`
Arguments as a JSON string. * Function call output `object` (returned by the client after executing the tool) * `type` `string`
Always `function_call_output`. * `call_id` `string`
The `call_id` of the matching `function_call`. * `output` `string`
Tool execution result (JSON string recommended). Image and video URLs must be publicly reachable from the server; if the server cannot fetch the URL, a parameter error will be returned. For image input, base64 data URLs are recommended to avoid authentication, hotlink protection, or network access failures on external URLs. * `instructions` `string` ***optional***
Top-level system instructions. * `stream` `bool` ***optional***
Whether to enable SSE streaming. Default is `false`. * `temperature` `float` ***optional***
Sampling temperature, between 0.0 and 2.0. * `top_p` `float` ***optional***
Nucleus sampling parameter. * `max_output_tokens` `int` ***optional***
Maximum number of output tokens for this response. `max_output_tokens` limits both the reasoning process and the final output. When using `medium` / `high` reasoning effort, JSON Schema, video, or other complex inputs, reserve a larger output budget; if the budget is insufficient, the response may return `status="incomplete"`, and `output` may contain only a `reasoning` item without a final `message`. * `reasoning` `object` ***optional***
Reasoning configuration. * `effort` `string` ***optional***
Reasoning effort level. Valid values: `low` / `medium` / `high`. * `tools` `object array` ***optional***
List of tool definitions. Currently only `function`-type tools are supported. * `type` `string`
Always `function`. * `name` `string`
Function name. Letters, digits, and `_-` are recommended. * `description` `string` ***optional***
Function description — helps the model decide whether to call the tool. * `parameters` `object`
JSON Schema describing the function arguments. * `strict` `boolean` ***optional***
Whether to enable strict mode. When enabled, the model's output arguments will strictly match the schema in `parameters`. * `tool_choice` `string or object` ***optional***
Tool-call strategy. Currently only the string `"auto"` is supported (the model decides whether to call a tool). * `text` `object` ***optional***
Text output format configuration. * `format` `object` * `type` `string`
One of `text`, `json_object`, or `json_schema`. * `name` `string` ***optional***
Required when `type=json_schema`. The schema's identifier name. * `strict` `boolean` ***optional***
Available when `type=json_schema`. When enabled, the model's output will strictly match the schema. * `schema` `object` ***optional***
Required when `type=json_schema`. An object following the [JSON Schema](https://json-schema.org/) specification. ## Response Format ### Non-streaming response When `stream=false` (default), a single Response object is returned. #### Properties * `id` `string`
Unique response ID, in the form `resp_xxx`. * `object` `string`
Always `response`. * `created_at` `int`
Creation time as a Unix timestamp (seconds). * `completed_at` `int or null`
Completion time as a Unix timestamp (seconds). * `status` `string`
Response status. One of `completed`, `incomplete`, or `failed`. * `error` `object or null`
Error information. Non-null only when `status=failed`. * `incomplete_details` `object or null`
Incomplete details. Non-null only when `status=incomplete`; commonly `{ "reason": "max_output_tokens" }`. * `model` `string`
The model ID actually used. * `output` `object array`
Array of output items. Each element may be of one of the following types: * Reasoning item `object` * `type` `string`
Always `reasoning`. * `id` `string`
Reasoning item ID. * `status` `string or null`
Reasoning item status; may be `completed` upon streaming completion. * `summary` `object array`
Reasoning summary list; usually an empty array. * `content` `object or null`
Reasoning content; usually `null`. * `encrypted_content` `string or null`
Encrypted reasoning content; usually `null`. * Text message `object` * `type` `string`
Always `message`. * `id` `string`
Message ID. * `role` `string`
Always `assistant`. * `status` `string`
Always `completed`. * `content` `object array` * `type` `string`
Always `output_text`. * `text` `string`
Text content generated by the model. * `annotations` `object array`
List of annotations. Empty array when there are no annotations. * Function call `object` * `type` `string`
Always `function_call`. * `id` `string`
Unique function-call ID. * `call_id` `string`
Correlation ID. Must be passed back with the `function_call_output`. * `name` `string`
Name of the tool being called. * `arguments` `string`
Arguments as a JSON string. * `status` `string`
Always `completed`. * `usage` `object`
Token usage statistics. * `input_tokens` `int`
Input token count. * `input_tokens_details` `object` * `cached_tokens` `int`
Input tokens served from cache. * `output_tokens` `int`
Output token count. * `output_tokens_details` `object` * `reasoning_tokens` `int`
Output tokens used for reasoning. * `tool_output_tokens` `int`
Output tokens used for tool output. * `total_tokens` `int`
Total token count. * `instructions` `string or null`
Echoes the top-level instructions from the request. * `max_output_tokens` `int or null`
Echoes the request parameter. * `reasoning` `object or null`
Echoes the reasoning configuration. * `temperature` `float or null`
Echoes the sampling temperature. * `top_p` `float or null`
Echoes the nucleus sampling parameter. * `text` `object`
Echoes the text output format configuration. * `format` `object` * `type` `string`
One of `text`, `json_object`, or `json_schema`. * `name` `string`
Present when `type=json_schema`. The schema's identifier name. * `strict` `boolean`
Present when `type=json_schema`. * `schema` `object`
Present when `type=json_schema`. An object following the [JSON Schema](https://json-schema.org/) specification. * `tool_choice` `string or object`
Echoes the tool-call strategy. * `tools` `object array`
Echoes the tool definitions. #### Example ```json theme={null} { "id": "resp_xxxxxxxxxxxxxxxx", "object": "response", "created_at": 1772624997, "completed_at": 1772624998, "model": "step-3.7-flash", "status": "completed", "error": null, "incomplete_details": null, "output": [ { "type": "reasoning", "id": "rs_xxxxxxxxxxxxxxxx", "summary": [], "content": null, "encrypted_content": null, "status": null }, { "type": "message", "id": "msg_xxxxxxxxxxxxxxxx", "status": "completed", "role": "assistant", "content": [ { "type": "output_text", "text": "Quantum computing is a new computational paradigm that leverages principles of quantum mechanics (such as superposition and entanglement) to process information.", "annotations": [] } ] } ], "usage": { "input_tokens": 14, "input_tokens_details": { "cached_tokens": 0 }, "output_tokens": 52, "output_tokens_details": { "reasoning_tokens": 0, "tool_output_tokens": 0 }, "total_tokens": 66 }, "instructions": null, "max_output_tokens": null, "reasoning": { "effort": "medium", "summary": null }, "temperature": 1.0, "top_p": 1.0, "text": { "format": { "type": "text" } }, "tool_choice": "auto", "tools": [] } ``` ### Streaming response When `stream=true`, Server-Sent Events (SSE) data is returned. Each event consists of an `event:` line and a `data:` line. Each event's `data` object contains `type` and `sequence_number`. `type` matches the `event` name; `sequence_number` starts from 0 and increments, allowing clients to process events in order. #### Event types | Event | Triggered when | | ---------------------------------------- | ------------------------------- | | `response.created` | Response is created | | `response.in_progress` | Generation begins | | `response.output_item.added` | A new output item is created | | `response.reasoning_part.added` | A reasoning content part begins | | `response.reasoning_text.delta` | Reasoning text delta | | `response.reasoning_text.done` | Reasoning text finishes | | `response.reasoning_part.done` | Reasoning content part finishes | | `response.content_part.added` | A text content part begins | | `response.output_text.delta` | Text delta | | `response.output_text.done` | Text part finishes | | `response.content_part.done` | Content part finishes | | `response.function_call_arguments.delta` | Tool argument delta | | `response.function_call_arguments.done` | Tool arguments finish | | `response.output_item.done` | Output item finishes | | `response.completed` | Response completes | | `response.incomplete` | Ended due to output truncation | | `response.failed` | Generation failed | | `error` | Transport-layer error | #### Example Text streaming: ```text theme={null} event: response.created data: {"type":"response.created","sequence_number":0,"response":{"id":"resp_xxx","object":"response","created_at":1772624997,"model":"step-3.7-flash","status":"in_progress","output":[]}} event: response.in_progress data: {"type":"response.in_progress","sequence_number":1,"response":{"id":"resp_xxx","status":"in_progress"}} event: response.output_item.added data: {"type":"response.output_item.added","sequence_number":2,"output_index":0,"item":{"id":"rs_xxx","type":"reasoning","summary":[],"content":null,"encrypted_content":null,"status":"in_progress"}} event: response.reasoning_part.added data: {"type":"response.reasoning_part.added","sequence_number":3,"output_index":0,"item_id":"rs_xxx","content_index":0,"part":{"type":"reasoning_text","text":""}} event: response.reasoning_text.delta data: {"type":"response.reasoning_text.delta","sequence_number":4,"output_index":0,"item_id":"rs_xxx","content_index":0,"delta":"User asked for a greeting."} event: response.reasoning_text.done data: {"type":"response.reasoning_text.done","sequence_number":5,"output_index":0,"item_id":"rs_xxx","content_index":0,"text":"User asked for a greeting."} event: response.reasoning_part.done data: {"type":"response.reasoning_part.done","sequence_number":6,"output_index":0,"item_id":"rs_xxx","content_index":0,"part":{"type":"reasoning_text","text":"User asked for a greeting."}} event: response.output_item.done data: {"type":"response.output_item.done","sequence_number":7,"output_index":0,"item":{"id":"rs_xxx","type":"reasoning","summary":[],"content":null,"encrypted_content":null,"status":"completed"}} event: response.output_item.added data: {"type":"response.output_item.added","sequence_number":8,"output_index":1,"item":{"id":"msg_xxx","type":"message","role":"assistant","status":"in_progress","content":[]}} event: response.content_part.added data: {"type":"response.content_part.added","sequence_number":9,"item_id":"msg_xxx","output_index":1,"content_index":0,"part":{"type":"output_text","text":"","annotations":[]}} event: response.output_text.delta data: {"type":"response.output_text.delta","sequence_number":10,"item_id":"msg_xxx","output_index":1,"content_index":0,"delta":"Hello"} event: response.output_text.done data: {"type":"response.output_text.done","sequence_number":11,"item_id":"msg_xxx","output_index":1,"content_index":0,"text":"Hello"} event: response.content_part.done data: {"type":"response.content_part.done","sequence_number":12,"item_id":"msg_xxx","output_index":1,"content_index":0,"part":{"type":"output_text","text":"Hello","annotations":[]}} event: response.output_item.done data: {"type":"response.output_item.done","sequence_number":13,"output_index":1,"item":{"id":"msg_xxx","type":"message","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Hello","annotations":[]}]}} event: response.completed data: {"type":"response.completed","sequence_number":14,"response":{"id":"resp_xxx","object":"response","status":"completed","output":[{"id":"rs_xxx","type":"reasoning","summary":[],"content":null,"encrypted_content":null,"status":"completed"},{"id":"msg_xxx","type":"message","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Hello","annotations":[]}]}],"usage":{"input_tokens":10,"output_tokens":2,"total_tokens":12}}} ``` Function-call streaming (excerpt): ```text theme={null} event: response.output_item.added data: {"type":"response.output_item.added","sequence_number":0,"output_index":0,"item":{"id":"fc_xxx","type":"function_call","call_id":"call_xxx","name":"get_weather","arguments":"","status":"in_progress"}} event: response.function_call_arguments.delta data: {"type":"response.function_call_arguments.delta","sequence_number":1,"item_id":"fc_xxx","output_index":0,"delta":"{\"city\":\"Beijing\"}"} event: response.function_call_arguments.done data: {"type":"response.function_call_arguments.done","sequence_number":2,"item_id":"fc_xxx","output_index":0,"arguments":"{\"city\":\"Beijing\"}","name":"get_weather"} event: response.output_item.done data: {"type":"response.output_item.done","sequence_number":3,"output_index":0,"item":{"id":"fc_xxx","type":"function_call","call_id":"call_xxx","name":"get_weather","arguments":"{\"city\":\"Beijing\"}","status":"completed"}} event: response.completed data: {"type":"response.completed","sequence_number":4,"response":{"id":"resp_xxx","object":"response","status":"completed","output":[{"id":"fc_xxx","type":"function_call","call_id":"call_xxx","name":"get_weather","arguments":"{\"city\":\"Beijing\"}","status":"completed"}]}} ``` ## Examples ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") response = client.responses.create( model="step-3.7-flash", input="Briefly introduce quantum computing in one sentence", ) print(response.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const response = await openai.responses.create({ model: "step-3.7-flash", input: "Briefly introduce quantum computing in one sentence", }); console.log(response.output_text); ``` ```bash theme={null} curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": "Briefly introduce quantum computing in one sentence" }' ``` First turn: the model issues a `function_call` based on the tool definition. Second turn: the client passes both the previous `function_call` and the locally executed `function_call_output` back, and the model produces the final answer based on the tool result. ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") tools = [{ "type": "function", "name": "get_weather", "description": "Get the current weather for a city", "parameters": { "type": "object", "properties": {"city": {"type": "string", "description": "City name"}}, "required": ["city"], }, "strict": True, }] # First turn: the model issues a function_call r1 = client.responses.create( model="step-3.7-flash", input=[{"role": "user", "content": "What's the weather in Beijing today?"}], tools=tools, tool_choice="auto", ) fc = next(o for o in r1.output if o.type == "function_call") # Client executes the tool (mocked here for demonstration) tool_result = '{"temperature":22,"weather":"sunny","humidity":45}' # Second turn: pass function_call and function_call_output back r2 = client.responses.create( model="step-3.7-flash", input=[ {"role": "user", "content": "What's the weather in Beijing today?"}, { "type": "function_call", "id": fc.id, "call_id": fc.call_id, "name": fc.name, "arguments": fc.arguments, }, { "type": "function_call_output", "call_id": fc.call_id, "output": tool_result, }, ], tools=tools, ) print(r2.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const tools = [{ type: "function", name: "get_weather", description: "Get the current weather for a city", parameters: { type: "object", properties: { city: { type: "string", description: "City name" } }, required: ["city"] }, strict: true }]; // First turn: the model issues a function_call const r1 = await openai.responses.create({ model: "step-3.7-flash", input: [{ role: "user", content: "What's the weather in Beijing today?" }], tools, tool_choice: "auto" }); const fc = r1.output.find(o => o.type === "function_call"); // Client executes the tool (mocked here for demonstration) const toolResult = '{"temperature":22,"weather":"sunny","humidity":45}'; // Second turn: pass function_call and function_call_output back const r2 = await openai.responses.create({ model: "step-3.7-flash", input: [ { role: "user", content: "What's the weather in Beijing today?" }, { type: "function_call", id: fc.id, call_id: fc.call_id, name: fc.name, arguments: fc.arguments }, { type: "function_call_output", call_id: fc.call_id, output: toolResult } ], tools }); console.log(r2.output_text); ``` ```bash theme={null} # First turn: the model issues a function_call curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": [{ "role": "user", "content": "What'\''s the weather in Beijing today?" }], "tools": [{ "type": "function", "name": "get_weather", "description": "Get the current weather for a city", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "City name" } }, "required": ["city"] }, "strict": true }], "tool_choice": "auto" }' # Second turn: pass function_call and function_call_output back (use the call_id returned by the previous turn) curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": [ { "role": "user", "content": "What'\''s the weather in Beijing today?" }, { "type": "function_call", "id": "fc_xxxxxxxxxxxxxxxx", "call_id": "call_xxxxxxxxxxxxxxxx", "name": "get_weather", "arguments": "{\"city\":\"Beijing\"}" }, { "type": "function_call_output", "call_id": "call_xxxxxxxxxxxxxxxx", "output": "{\"temperature\":22,\"weather\":\"sunny\",\"humidity\":45}" } ], "tools": [{ "type": "function", "name": "get_weather", "parameters": { "type": "object", "properties": { "city": { "type": "string" } }, "required": ["city"] }, "strict": true }] }' ``` Base64 data URLs are recommended: ```python theme={null} import base64, requests from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") # Load the image into memory — demo only; adapt to reading from disk as needed r = requests.get("https://www.stepfun.com/assets/section-1-CTe4nZiO.webp") r.raise_for_status() image_data_url = "data:image/webp;base64," + base64.b64encode(r.content).decode("ascii") response = client.responses.create( model="step-3.7-flash", input=[{ "role": "user", "content": [ {"type": "input_text", "text": "Describe the content of this image"}, {"type": "input_image", "image_url": image_data_url}, ], }], ) print(response.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); // Load the image into memory — demo only; adapt to reading from disk as needed async function loadImage(url) { const res = await fetch(url); const blob = await res.blob(); const buffer = Buffer.from(await blob.arrayBuffer()); return "data:" + blob.type + ";base64," + buffer.toString("base64"); } const imageDataUrl = await loadImage("https://www.stepfun.com/assets/section-1-CTe4nZiO.webp"); const response = await openai.responses.create({ model: "step-3.7-flash", input: [{ role: "user", content: [ { type: "input_text", text: "Describe the content of this image" }, { type: "input_image", image_url: imageDataUrl } ] }] }); console.log(response.output_text); ``` ```bash theme={null} # Load the image into memory — demo only; adapt to reading from disk as needed image_base64="data:image/webp;base64,"$(curl -s "https://www.stepfun.com/assets/section-1-CTe4nZiO.webp" | base64) curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d "{ \"model\": \"step-3.7-flash\", \"input\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"input_text\", \"text\": \"Describe the content of this image\" }, { \"type\": \"input_image\", \"image_url\": \"${image_base64}\" } ] } ] }" ``` You can also use the object form to specify `detail`: ```json theme={null} { "type": "input_image", "image_url": { "url": "data:image/jpeg;base64,...", "detail": "high" } } ``` Video is passed via URL; the URL must be publicly reachable from the server: ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") response = client.responses.create( model="step-3.7-flash", input=[{ "role": "user", "content": [ {"type": "input_text", "text": "Summarize the main content of this video"}, {"type": "input_video", "video_url": {"url": "https://example.com/demo.mp4", "detail": "low"}}, ], }], ) print(response.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const response = await openai.responses.create({ model: "step-3.7-flash", input: [{ role: "user", content: [ { type: "input_text", text: "Summarize the main content of this video" }, { type: "input_video", video_url: { url: "https://example.com/demo.mp4", detail: "low" } } ] }] }); console.log(response.output_text); ``` ```bash theme={null} curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": [ { "role": "user", "content": [ { "type": "input_text", "text": "Summarize the main content of this video" }, { "type": "input_video", "video_url": {"url": "https://example.com/demo.mp4", "detail": "low"} } ] } ] }' ``` ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") response = client.responses.create( model="step-3.7-flash", input="Analyze the sentiment of this sentence: This movie is fantastic, highly recommended!", text={ "format": { "type": "json_schema", "name": "sentiment_analysis", "strict": True, "schema": { "type": "object", "properties": { "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]}, "confidence": {"type": "number"}, "keywords": {"type": "array", "items": {"type": "string"}}, }, "required": ["sentiment", "confidence", "keywords"], "additionalProperties": False, }, } }, ) print(response.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const response = await openai.responses.create({ model: "step-3.7-flash", input: "Analyze the sentiment of this sentence: This movie is fantastic, highly recommended!", text: { format: { type: "json_schema", name: "sentiment_analysis", strict: true, schema: { type: "object", properties: { sentiment: { type: "string", enum: ["positive", "negative", "neutral"] }, confidence: { type: "number" }, keywords: { type: "array", items: { type: "string" } } }, required: ["sentiment", "confidence", "keywords"], additionalProperties: false } } } }); console.log(response.output_text); ``` ```bash theme={null} curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": "Analyze the sentiment of this sentence: This movie is fantastic, highly recommended!", "text": { "format": { "type": "json_schema", "name": "sentiment_analysis", "strict": true, "schema": { "type": "object", "properties": { "sentiment": { "type": "string", "enum": ["positive", "negative", "neutral"] }, "confidence": { "type": "number" }, "keywords": { "type": "array", "items": { "type": "string" } } }, "required": ["sentiment", "confidence", "keywords"], "additionalProperties": false } } } }' ``` ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") stream = client.responses.create( model="step-3.7-flash", input="Write a haiku about spring", stream=True, ) for event in stream: if event.type == "response.output_text.delta": print(event.delta, end="", flush=True) print() ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const stream = await openai.responses.create({ model: "step-3.7-flash", input: "Write a haiku about spring", stream: true }); for await (const event of stream) { if (event.type === "response.output_text.delta") { process.stdout.write(event.delta); } } process.stdout.write("\n"); ``` ```bash theme={null} curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": "Write a haiku about spring", "stream": true }' ``` See the "Streaming response" section above for the streaming response format. ```python theme={null} from openai import OpenAI client = OpenAI(api_key="STEP_API_KEY", base_url="https://api.stepfun.ai/v1") response = client.responses.create( model="step-3.7-flash", input="Prove: the remainder of any perfect square modulo 4 can only be 0 or 1", reasoning={"effort": "high"}, ) print(response.output_text) ``` ```js theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "STEP_API_KEY", baseURL: "https://api.stepfun.ai/v1" }); const response = await openai.responses.create({ model: "step-3.7-flash", input: "Prove: the remainder of any perfect square modulo 4 can only be 0 or 1", reasoning: { effort: "high" } }); console.log(response.output_text); ``` ```bash theme={null} curl https://api.stepfun.ai/v1/responses \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $STEP_API_KEY" \ -d '{ "model": "step-3.7-flash", "input": "Prove: the remainder of any perfect square modulo 4 can only be 0 or 1", "reasoning": { "effort": "high" } }' ```