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Anthropic-compatible Messages API ​

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Migrate your Anthropic application to Model Studio by changing three settings. This topic covers the request and response parameters with code examples.

To migrate an existing Anthropic application to Model Studio, change these settings:

  • api_key: Replace with the Model Studio API key.

  • base_url: Replace with a Model Studio endpoint listed below.

  • model: Replace with a supported model name, such as qwen3.7-plus.

Singapore

China (Beijing)

US (Virginia)

Germany (Frankfurt)

SDK base_url:https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic

HTTP request URL:POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages

Replace {WorkspaceId} with your actual workspace ID.

Important

The legacy Singapore URL https://dashscope-intl.aliyuncs.com/apps/anthropic will be deprecated soon. Please migrate to the new URL https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic.

SDK base_url:https://dashscope.aliyuncs.com/apps/anthropic

HTTP request URL:POST https://dashscope.aliyuncs.com/apps/anthropic/v1/messages

SDK base_url:https://dashscope-us.aliyuncs.com/apps/anthropic

HTTP request URL:POST https://dashscope-us.aliyuncs.com/apps/anthropic/v1/messages

SDK base_url:https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/apps/anthropic

HTTP request URL:POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/apps/anthropic/v1/messages

Replace {WorkspaceId} with your actual Workspace ID.

Authentication: Pass your Model Studio API key in either the x-api-key header or the Authorization: Bearer header.

## Request BodyBasic Call Streaming Extended Thinking Image Understanding Video Understanding Function calling Prompt Caching Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) message = client.messages.create( model="qwen3.7-plus", max_tokens=1024, system="You are a helpful assistant", messages=[ { "role": "user", "content": "Who are you?" } ], thinking={"type": "disabled"}, ) print(message.content[0].text) typescript import Anthropic from "@anthropic-ai/sdk"; const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); async function main() { const message = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 1024, system: "You are a helpful assistant", messages: [{ role: "user", content: "Who are you?" }], thinking: { type: "disabled" }, }); console.log(message.content[0].text); } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "system": "You are a helpful assistant", "messages": [ { "role": "user", "content": "Who are you?" } ], "thinking": {"type": "disabled"} }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) stream = client.messages.create( model="qwen3.7-plus", max_tokens=1024, stream=True, messages=[ { "role": "user", "content": "Give a brief introduction to artificial intelligence." } ], thinking={"type": "disabled"}, ) for chunk in stream: if chunk.type == "content_block_delta": if hasattr(chunk.delta, 'text'): print(chunk.delta.text, end="", flush=True) typescript import Anthropic from "@anthropic-ai/sdk"; async function main() { const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); const stream = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 1024, stream: true, messages: [{ role: "user", content: "Give a brief introduction to artificial intelligence." }], thinking: { type: "disabled" }, }); for await (const chunk of stream) { if (chunk.type === "content_block_delta" && 'text' in chunk.delta) { process.stdout.write(chunk.delta.text); } } } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ --no-buffer \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "stream": true, "messages": [ { "role": "user", "content": "Give a brief introduction to artificial intelligence." } ], "thinking": {"type": "disabled"} }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) stream = client.messages.create( model="qwen3.7-plus", max_tokens=2048, stream=True, thinking={ "type": "enabled", "budget_tokens": 1024 }, messages=[ { "role": "user", "content": "Analyze the future prospects of quantum computing." } ] ) for chunk in stream: if chunk.type == "content_block_delta": if hasattr(chunk.delta, 'thinking'): print(chunk.delta.thinking, end="", flush=True) elif hasattr(chunk.delta, 'text'): print(chunk.delta.text, end="", flush=True) typescript import Anthropic from "@anthropic-ai/sdk"; async function main() { const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); const stream = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 2048, stream: true, thinking: { type: "enabled", budget_tokens: 1024 }, messages: [{ role: "user", content: "Analyze the future prospects of quantum computing." }] }); for await (const chunk of stream) { if (chunk.type === "content_block_delta") { if ('thinking' in chunk.delta) { process.stdout.write(chunk.delta.thinking); } else if ('text' in chunk.delta) { process.stdout.write(chunk.delta.text); } } } } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 2048, "stream": true, "thinking": { "type": "enabled", "budget_tokens": 1024 }, "messages": [ { "role": "user", "content": "Analyze the future prospects of quantum computing." } ] }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) stream = client.messages.create( model="qwen3.7-plus", max_tokens=1024, stream=True, messages=[ { "role": "user", "content": [ { "type": "image", "source": { "type": "url", "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250414/mqqmiy/animal_01.jpg", }, }, { "type": "text", "text": "Describe the content of this image." }, ], } ], thinking={"type": "disabled"}, ) for chunk in stream: if chunk.type == "content_block_delta": if hasattr(chunk.delta, 'text'): print(chunk.delta.text, end="", flush=True) typescript import Anthropic from "@anthropic-ai/sdk"; async function main() { const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); const stream = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 1024, stream: true, messages: [{ role: "user", content: [ { type: "image", source: { type: "url", url: "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250414/mqqmiy/animal_01.jpg", }, }, { type: "text", text: "Describe the content of this image." }, ], }], thinking: { type: "disabled" }, }); for await (const chunk of stream) { if (chunk.type === "content_block_delta" && 'text' in chunk.delta) { process.stdout.write(chunk.delta.text); } } } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "stream": true, "messages": [ { "role": "user", "content": [ { "type": "image", "source": { "type": "url", "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250414/mqqmiy/animal_01.jpg" } }, { "type": "text", "text": "Describe the content of this image." } ] } ], "thinking": {"type": "disabled"} }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) stream = client.messages.create( model="qwen3.7-plus", max_tokens=1024, stream=True, messages=[ { "role": "user", "content": [ { "type": "video", "source": { "type": "url", "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251208/zpupby/3e81ef38-98f0-4d55-bbb6-259334ca18d0.mp4", }, }, { "type": "text", "text": "Describe the content of this video." }, ], } ], thinking={"type": "disabled"}, ) for chunk in stream: if chunk.type == "content_block_delta": if hasattr(chunk.delta, 'text'): print(chunk.delta.text, end="", flush=True) typescript import Anthropic from "@anthropic-ai/sdk"; async function main() { const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); const stream = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 1024, stream: true, messages: [{ role: "user", content: [ { type: "video", source: { type: "url", url: "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251208/zpupby/3e81ef38-98f0-4d55-bbb6-259334ca18d0.mp4", }, }, { type: "text", text: "Describe the content of this video." }, ], }], thinking: { type: "disabled" }, }); for await (const chunk of stream) { if (chunk.type === "content_block_delta" && 'text' in chunk.delta) { process.stdout.write(chunk.delta.text); } } } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "stream": true, "messages": [ { "role": "user", "content": [ { "type": "video", "source": { "type": "url", "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251208/zpupby/3e81ef38-98f0-4d55-bbb6-259334ca18d0.mp4" } }, { "type": "text", "text": "Describe the content of this video." } ] } ], "thinking": {"type": "disabled"} }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) tools = [ { "name": "get_weather", "description": "Get weather information for a specified city", "input_schema": { "type": "object", "properties": { "city": { "type": "string", "description": "City name" } }, "required": ["city"] } } ] message = client.messages.create( model="qwen3.7-plus", max_tokens=1024, tools=tools, messages=[ { "role": "user", "content": "What's the weather like in Hangzhou today?" } ] ) print(message.content) typescript import Anthropic from "@anthropic-ai/sdk"; async function main() { const anthropic = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); const message = await anthropic.messages.create({ model: "qwen3.7-plus", max_tokens: 1024, tools: [ { name: "get_weather", description: "Get weather information for a specified city", input_schema: { type: "object", properties: { city: { type: "string", description: "City name" } }, required: ["city"], }, }, ], messages: [{ role: "user", content: "What's the weather like in Hangzhou today?" }], }); console.log(JSON.stringify(message.content, null, 2)); } main().catch(console.error); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "tools": [ { "name": "get_weather", "description": "Get weather information for a specified city", "input_schema": { "type": "object", "properties": { "city": { "type": "string", "description": "City name" } }, "required": ["city"] } } ], "messages": [ { "role": "user", "content": "What's the weather like in Hangzhou today?" } ] }' Python TypeScript curl python import anthropic import os client = anthropic.Anthropic( api_key=os.getenv("DASHSCOPE_API_KEY"), # Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", ) # Simulate code repository content. Must reach minimum cacheable length (1024 tokens) long_text_content = "<Your Code Here>" * 400 def get_completion(user_input): response = client.messages.create( # Choose a model that supports prompt caching model="qwen3.7-plus", max_tokens=1024, system=[ { "type": "text", "text": long_text_content, # Add cache_control on a text block to mark a cache breakpoint. Can also be placed on content blocks in the messages array "cache_control": {"type": "ephemeral"}, } ], messages=[ {"role": "user", "content": user_input}, ], ) return response # First request: Create cache first = get_completion("What does this code do?") print(f"Cache creation tokens: {first.usage.cache_creation_input_tokens}") print(f"Cache read tokens: {first.usage.cache_read_input_tokens}") print("=" * 20) # Second request: Same long content, different question -> Cache hit second = get_completion("How can this code be optimized?") print(f"Cache creation tokens: {second.usage.cache_creation_input_tokens}") print(f"Cache read tokens: {second.usage.cache_read_input_tokens}") typescript import Anthropic from "@anthropic-ai/sdk"; const client = new Anthropic({ apiKey: process.env.DASHSCOPE_API_KEY, // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic", }); // Simulate code repository content. Must reach minimum cacheable length (1024 tokens) const longTextContent = "<Your Code Here>".repeat(400); async function getCompletion(userInput) { return client.messages.create({ // Choose a model that supports prompt caching model: "qwen3.7-plus", max_tokens: 1024, system: [ { type: "text", text: longTextContent, // Add cache_control on a text block to mark a cache breakpoint. Can also be placed on content blocks in the messages array cache_control: { type: "ephemeral" }, }, ], messages: [{ role: "user", content: userInput }], }); } // First request: Create cache const first = await getCompletion("What does this code do?"); console.log(`Cache creation tokens: ${first.usage.cache_creation_input_tokens}`); console.log(`Cache read tokens: ${first.usage.cache_read_input_tokens}`); console.log("=".repeat(20)); // Second request: Same long content, different question -> Cache hit const second = await getCompletion("How can this code be optimized?"); console.log(`Cache creation tokens: ${second.usage.cache_creation_input_tokens}`); console.log(`Cache read tokens: ${second.usage.cache_read_input_tokens}`); curl curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages" \ -H "Content-Type: application/json" \ -H "x-api-key: $DASHSCOPE_API_KEY" \ -d '{ "model": "qwen3.7-plus", "max_tokens": 1024, "system": [ { "type": "text", "text": "<Place cacheable content here with at least 1024 tokens>", "cache_control": {"type": "ephemeral"} } ], "messages": [ {"role": "user", "content": "What does this code do?"} ] }'
modelstring (Required) Model name. Supported models: Supported Models Qwen-Max: qwen3.7-max, qwen3.7-max-2026-05-20, qwen3.7-max-2026-06-08, qwen3.6-max-preview, qwen3-max, qwen3-max-2026-01-23, qwen3-max-preview Qwen-Plus: qwen3.7-plus, qwen3.7-plus-2026-05-26, qwen3.6-plus, qwen3.6-plus-2026-04-02, qwen3.5-plus, qwen3.5-plus-2026-04-20, qwen3.5-plus-2026-02-15, qwen-plus, qwen-plus-latest, qwen-plus-2025-09-11 Qwen-Flash: qwen3.6-flash, qwen3.6-flash-2026-04-16, qwen3.5-flash, qwen3.5-flash-2026-02-23, qwen-flash, qwen-flash-2025-07-28 Qwen-Turbo: qwen-turbo Qwen-Coder: qwen3-coder-next, qwen3-coder-plus, qwen3-coder-plus-2025-09-23, qwen3-coder-flash Qwen-VL: qwen3-vl-plus, qwen3-vl-flash, qwen-vl-max, qwen-vl-plus Qwen Open-Source Models: qwen3.6-27b, qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-27b, qwen3.5-35b-a3b Third-Party Models deepseek-v4-pro, deepseek-v4-flash, kimi-k2.5, kimi-k2-thinking, glm-5.1, glm-5, glm-4.7, glm-4.6, MiniMax-M2.5, MiniMax-M2.1
max_tokensinteger (Required) Maximum number of tokens for the reply content. If the generated content exceeds this value, generation stops early and stop_reason is max_tokens. > max_tokens does not limit the length of the thinking process. When extended thinking is enabled, the thinking tokens are controlled separately by thinking.budget_tokens.
systemstring or array (Optional) System prompt that defines model behavior. system is a top-level parameter — the messages array does not accept a system role. A string equals a single type="text" block. Pass an array to mark prompt caching breakpoints. Properties typestring (Required) Fixed value: text. textstring (Required) The system prompt text. cache_controlobject (Optional) Prompt caching breakpoint. On cache hit, subsequent requests are billed at the cache read rate. Contains only type, fixed to ephemeral.
messagesarray (Required) The message array, arranged in alternating user/assistant turns. messages array element rolestring (Required) The message role. Valid values: user, assistant. contentstring or array (Required) Plain text string or structured content array. A string equals a single content block with type="text". content array element types Text Properties typestring (Required) Fixed value: text. textstring (Required) The text content. cache_controlobject (Optional) Prompt caching breakpoint. Contains only type, fixed to ephemeral. Image (requires vision model) Properties typestring (Required) Fixed value: image. sourceobject (Required) The source of the image data. Properties typestring (Required) Valid values: url (public image URL), base64 (Base64-encoded). urlstring The public URL of the image. Required when type is url. media_typestring The MIME type of the image, such as image/jpeg. Required when type is base64. datastring The Base64-encoded image data. Required when type is base64. Video (requires vision model) Properties typestring (Required) Fixed value: video. sourceobject (Required) The source of the video data. Properties typestring (Required) Valid values: url (public video URL), base64 (Base64-encoded). urlstring The public URL of the video. Required when type is url. media_typestring The MIME type of the video, such as video/mp4. Required when type is base64. datastring The Base64-encoded video data. Required when type is base64. Tool use (assistant role; tool call instruction returned by the model) Properties typestring (Required) Fixed value: tool_use. idstring (Required) The unique identifier of the tool call, used to associate the result in a subsequent tool_result. namestring (Required) The name of the called tool. inputobject (Required) The input parameters of the tool call. The structure is determined by the input_schema of the corresponding tool in tools. cache_controlobject (Optional) Prompt caching breakpoint. Contains only type, fixed to ephemeral. The tool call content participates in the cache prefix. Tool result (user role; execution result of a tool sent back to the model) Properties typestring (Required) Fixed value: tool_result. tool_use_idstring (Required) Corresponds to the id in the tool_use block. contentstring (Required) Content returned by the tool. cache_controlobject (Optional) Prompt caching breakpoint. Contains only type, fixed to ephemeral.
streamboolean (Optional) Whether to enable streaming. Default value: false.
temperaturenumber (Optional) Controls the diversity of generated text. Value range: [0, 2). Higher values produce more random results. Note This range is different from the official Anthropic range of [0.0, 1.0]. When migrating from Anthropic, verify the value of this parameter.\
top_pnumber (Optional) Nucleus sampling probability threshold. > Both temperature and top_p can control the diversity of generated text. We recommend setting only one of them. For more information, see Overview.\
top_kinteger (Optional) Candidate set size during sampling.\
stop_sequencesarray (Optional) Text sequences that trigger generation to stop. Output ends before the matched sequence. Note After a match, the stop_reason in the response is still end_turn, and the response does not include the matched sequence.\
thinkingobject (Optional) Extended thinking configuration. When enabled, the model reasons before responding, and the response includes thinking-type content blocks. Not all models support thinking mode. Properties typestring (Required) Valid values: enabled (enable thinking mode), disabled (disable thinking mode). budget_tokensinteger (Optional) Maximum tokens for the thinking process. Disjoint from max_tokens: this parameter limits the thinking portion, while max_tokens limits the final reply. A larger budget allows more thorough analysis on complex questions. Takes effect when type is enabled.\
reasoning_effortstring (Optional) Controls the reasoning intensity of the model. Valid values: high, max. Default value: max. Supported models: deepseek-v4-pro, deepseek-v4-flash. Note When set to low or medium, it is mapped to high. When set to xhigh, it is mapped to max.\
toolsarray (Optional) Tool definitions for function calling. tools array element namestring (Required) The tool name. descriptionstring (Optional) The description of the tool function. input_schemaobject (Required) The JSON Schema definition of the tool input parameters.\
tool_choiceobject (Optional) Tool selection strategy: - {"type": "auto"}: The model decides whether to call a tool (default). - {"type": "any"}: Force the model to call any tool. - {"type": "none"}: Prohibit the model from calling tools. - {"type": "tool", "name": "tool_name"}: Force the model to call a specified tool.\
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## Non-streaming ResponseResponse Example json { "id": "msg_e2898f19-fc0e-4cb3-bd9b-5b7dc4ea3bc9", "type": "message", "role": "assistant", "model": "qwen3.7-plus", "content": [ { "type": "thinking", "thinking": "Let me analyze this problem...", "signature": "" }, { "type": "text", "text": "Hello! I am Qwen..." } ], "stop_reason": "end_turn", "stop_sequence": null, "usage": { "input_tokens": 22, "output_tokens": 223, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0 } }
idstring Unique message identifier.\
typestring Fixed value: message.\
rolestring Fixed value: assistant.\
modelstring The model used for generation.\
contentarray The content array. content array element types Text Properties typestring Fixed value: text. textstring The text response generated by the model. Thinking (returned when Extended Thinking is enabled) Properties typestring Fixed value: thinking. thinkingstring The model's reasoning before the final response. signaturestring Currently fixed as an empty string. Tool use (function call scenario) Properties typestring Fixed value: tool_use. idstring Unique tool call identifier, used to match the tool_result. namestring The name of the called tool. inputobject The input parameters of the tool call.\
stop_reasonstring Reason generation stopped. Valid values: end_turn (normal completion), max_tokens (token limit reached), tool_use (tool call).\
stop_sequencestring Always null.\
usageobject Token usage statistics. Note In streaming calls, the usage field of the message_start event contains only input_tokens and output_tokens. The full four fields are returned in the message_delta event. Properties input_tokensinteger Input tokens. output_tokensinteger Output tokens. cache_creation_input_tokensinteger Tokens consumed for cache creation. cache_read_input_tokensinteger Tokens consumed from cache reads.
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## Streaming ResponseStreaming response example json {"type":"message_start","message":{"id":"msg_xxx","type":"message","role":"assistant","model":"qwen3.7-plus","content":[],"usage":{"input_tokens":15,"output_tokens":0}}} {"type":"content_block_start","index":0,"content_block":{"type":"thinking","thinking":"","signature":""}} {"type":"content_block_delta","index":0,"delta":{"type":"thinking_delta","thinking":"Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Artificial Intelligence (AI)\n - **Request:** Give a brief introduction to artificial intelligence."}} {"type":"content_block_delta","index":0,"delta":{"type":"signature_delta","signature":""}} {"type":"content_block_stop","index":0} {"type":"content_block_start","index":1,"content_block":{"type":"text","text":""}} {"type":"content_block_delta","index":1,"delta":{"type":"text_delta","text":"Artificial intelligence (AI) is an important branch of computer science..."}} {"type":"content_block_stop","index":1} {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"input_tokens":15,"output_tokens":1078,"cache_creation_input_tokens":0,"cache_read_input_tokens":0}} {"type":"message_stop"}
message_start First stream event, marks message start. Properties typestring Fixed value: message_start. messageobject The initial message object. content is an empty array, and usage contains only input_tokens and output_tokens.\
content_block_start Marks the start of a content block. Properties typestring Fixed value: content_block_start. indexinteger 0-based index corresponding to position in the content array. content_blockobject The initial object of the content block. The type value is text, thinking, or tool_use. For the tool_use type, the input field is an empty object in this event, and the complete input parameters are assembled from subsequent content_block_delta deltas.\
content_block_delta Incremental content block update. Multiple deltas sent per block. Properties typestring Fixed value: content_block_delta. indexinteger The index of the associated content block. deltaobject Delta object. type values: - text_delta: Text delta, containing the text field. - thinking_delta: Thinking delta, containing the thinking field. - signature_delta: Signature delta, containing the signature field (currently fixed as an empty string). - input_json_delta: Tool call input parameter delta, containing the partial_json field.\
content_block_stop Marks the end of a content block. Properties typestring Fixed value: content_block_stop. indexinteger The index of the ended content block.\
message_delta Sent after all content blocks end. Contains stop reason and final token usage. Properties typestring Fixed value: message_delta. deltaobject Contains stop_reason and stop_sequence. For valid values, see the Non-streaming Response table above. usageobject Complete token usage statistics, including input_tokens, output_tokens, cache_creation_input_tokens, and cache_read_input_tokens.\
message_stop Final event, marks message end. Properties typestring Fixed value: message_stop. In addition, streaming responses periodically send ping events ({"type":"ping"}) to keep the connection alive. Clients can ignore them.\
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