OpenAI Chat API reference

Reference, synced 2026-06-13.

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Call models using the OpenAI-compatible Chat API, including input and output parameter descriptions and code examples.

Singapore

US (Virginia)

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Germany (Frankfurt)

base_url for SDK: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1

HTTP endpoint: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Replace WorkspaceId with your actual Workspace ID.

Important

The legacy Singapore URL https://dashscope-intl.aliyuncs.com/compatible-mode/v1 will be deprecated soon. Please migrate to the new URL https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1.

base_url for SDK: https://dashscope-us.aliyuncs.com/compatible-mode/v1

HTTP endpoint: POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions

base_url for SDK: https://dashscope.aliyuncs.com/compatible-mode/v1

HTTP endpoint: POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

base_url for SDK: https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1

HTTP endpoint: POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Replace WorkspaceId with your actual Workspace ID.

Important

The legacy China (Hong Kong) URL https://cn-hongkong.dashscope.aliyuncs.com/compatible-mode/v1 will be deprecated soon. Please migrate to the new URL https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1 as soon as possible.

base_url for SDK: https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/compatible-mode/v1

HTTP endpoint: POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Replace WorkspaceId with your actual Workspace ID.

Prerequisites: Get an API key and set it as an environment variable. If you use the OpenAI SDK, install the SDK.

Note

Regional API keys: API keys differ by region. See Obtain an API key for details. In code examples, replace DASHSCOPE_API_KEY with your actual key if not using environment variables.


## Request body	POST/chat/completions Debug ## OpenAI-compatible API online debugging × International (Singapore) Chinese Mainland (Beijing) POSThttps://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions API Key Bearer Token Get API Key for International (Singapore) region Request body DefaultDeep thinkingImage inputTool callingStructured output Send request Clear response Response result Raw responseParsed content Text input Streaming output Image input Video input Tool calling Asynchronous invocation Python Java Node.js Go C# (HTTP) PHP (HTTP) curl python import os from openai import OpenAI client = OpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" 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/compatible-mode/v1", ) completion = client.chat.completions.create( model="qwen-plus", # Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ], # extra_body={"enable_thinking": False}, ) print(completion.model_dump_json()) java // This code uses OpenAI SDK version 2.6.0 import com.openai.client.OpenAIClient; import com.openai.client.okhttp.OpenAIOkHttpClient; import com.openai.models.chat.completions.ChatCompletion; import com.openai.models.chat.completions.ChatCompletionCreateParams; public class Main { public static void main(String[] args) { OpenAIClient client = OpenAIOkHttpClient.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. .baseUrl("https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1") .build(); ChatCompletionCreateParams params = ChatCompletionCreateParams.builder() .addUserMessage("Who are you?") .model("qwen-plus") .build(); try { ChatCompletion chatCompletion = client.chat().completions().create(params); System.out.println(chatCompletion); } catch (Exception e) { System.err.println("Error occurred: " + e.getMessage()); e.printStackTrace(); } } } nodejs import OpenAI from "openai"; const openai = new OpenAI( { // If the environment variable is not set, replace the following line with: apiKey: "sk-xxx", 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/compatible-mode/v1" } ); async function main() { const completion = await openai.chat.completions.create({ model: "qwen-plus", //Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "Who are you?" } ], }); console.log(JSON.stringify(completion)) } main(); go package main import ( "context" "os" "github.com/openai/openai-go" "github.com/openai/openai-go/option" ) func main() { client := openai.NewClient( option.WithAPIKey(os.Getenv("DASHSCOPE_API_KEY")), // defaults to os.LookupEnv("OPENAI_API_KEY") // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. option.WithBaseURL("https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/"), ) chatCompletion, err := client.Chat.Completions.New( context.TODO(), openai.ChatCompletionNewParams{ Messages: openai.F( []openai.ChatCompletionMessageParamUnion{ openai.UserMessage("Who are you?"), }, ), Model: openai.F("qwen-plus"), }, ) if err != nil { panic(err.Error()) } println(chatCompletion.Choices[0].Message.Content) } csharp using System.Net.Http.Headers; using System.Text; class Program { private static readonly HttpClient httpClient = new HttpClient(); static async Task Main(string[] args) { // If the environment variable is not set, replace the following line with: string? apiKey = "sk-xxx"; string? apiKey = Environment.GetEnvironmentVariable("DASHSCOPE_API_KEY"); if (string.IsNullOrEmpty(apiKey)) { Console.WriteLine("API Key is not set. Make sure the 'DASHSCOPE_API_KEY' environment variable is set."); return; } // Set the request URL and content // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. string url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions"; // Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models string jsonContent = @"{ ""model"": ""qwen-plus"", ""messages"": [ { ""role"": ""system"", ""content"": ""You are a helpful assistant."" }, { ""role"": ""user"", ""content"": ""Who are you?"" } ] }"; string result = await SendPostRequestAsync(url, jsonContent, apiKey); Console.WriteLine(result); } private static async Task<string> SendPostRequestAsync(string url, string jsonContent, string apiKey) { using (var content = new StringContent(jsonContent, Encoding.UTF8, "application/json")) { httpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey); httpClient.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json")); HttpResponseMessage response = await httpClient.PostAsync(url, content); if (response.IsSuccessStatusCode) { return await response.Content.ReadAsStringAsync(); } else { return $"Request failed: {response.StatusCode}"; } } } } php <?php // Set the request URL // Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. $url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions'; // If the environment variable is not set, replace the following line with: $apiKey = "sk-xxx"; $apiKey = getenv('DASHSCOPE_API_KEY'); $headers = [ 'Authorization: Bearer '.$apiKey, 'Content-Type: application/json' ]; $data = [ // Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models "model" => "qwen-plus", "messages" => [ [ "role" => "system", "content" => "You are a helpful assistant." ], [ "role" => "user", "content" => "Who are you?" ] ] ]; $ch = curl_init(); curl_setopt($ch, CURLOPT_URL, $url); curl_setopt($ch, CURLOPT_POST, true); curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data)); curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); curl_setopt($ch, CURLOPT_HTTPHEADER, $headers); $response = curl_exec($ch); if (curl_errno($ch)) { echo 'Curl error: ' . curl_error($ch); } curl_close($ch); echo $response; ?> > Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. See Get an API key. `curl curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen-plus", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Who are you?" } ] }'` > See Streaming output. Python Node.js curl python import os from openai import OpenAI client = OpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", ) completion = client.chat.completions.create( model="qwen-plus", # Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages=[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'Who are you?'},], stream=True, stream_options={"include_usage": True} ) for chunk in completion: print(chunk.model_dump_json()) nodejs import OpenAI from "openai"; const openai = new OpenAI( { apiKey: process.env.DASHSCOPE_API_KEY, baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" } ); async function main() { const completion = await openai.chat.completions.create({ model: "qwen-plus", // Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages: [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"} ], stream: true, }); for await (const chunk of completion) { console.log(JSON.stringify(chunk)); } } main(); > Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. See Get an API key. `curl curl --location "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions" \ --header "Authorization: Bearer $DASHSCOPE_API_KEY" \ --header "Content-Type: application/json" \ --data '{ "model": "qwen-plus", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Who are you?" } ], "stream":true }'` > For more image analysis options, see Image and video understanding. Python Node.js curl python import os from openai import OpenAI client = OpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", ) completion = client.chat.completions.create( model="qwen-vl-plus", # Model list: https://www.alibabacloud.com/help/en/model-studio/models messages=[{"role": "user","content": [ {"type": "image_url", "image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}, {"type": "text", "text": "What is this?"}, ]}] ) print(completion.model_dump_json()) nodejs import OpenAI from "openai"; const openai = new OpenAI( { apiKey: process.env.DASHSCOPE_API_KEY, baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" } ); async function main() { const response = await openai.chat.completions.create({ model: "qwen-vl-max", // Model list: https://www.alibabacloud.com/help/en/model-studio/models messages: [{role: "user",content: [ { type: "image_url",image_url: {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}, { type: "text", text: "What is this?" }, ]}] }); console.log(JSON.stringify(response)); } main(); > Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. See Get an API key. `curl curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen-vl-plus", "messages": [{ "role": "user", "content": [ {"type": "image_url","image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}, {"type": "text","text": "What is this?"} ]}] }'` > Example: Using an image list as video input. For other methods (video files), see Visual understanding. Python Node.js curl python import os from openai import OpenAI client = OpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", ) completion = client.chat.completions.create( # Model list: https://www.alibabacloud.com/help/en/model-studio/models model="qwen-vl-max", messages=[{ "role": "user", "content": [ { "type": "video", "video": [ "https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg"] }, { "type": "text", "text": "Describe the process in this video." }]}] ) print(completion.model_dump_json()) nodejs // Make sure you have specified "type": "module" in package.json. import OpenAI from "openai"; const openai = new OpenAI({ // If the environment variable is not set, replace the following line with: apiKey: "sk-xxx", // API keys differ by region. Replace {WorkspaceId} with your actual workspace ID. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key apiKey: process.env.DASHSCOPE_API_KEY, baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" }); async function main() { const response = await openai.chat.completions.create({ // Model list: https://www.alibabacloud.com/help/en/model-studio/models model: "qwen-vl-max", messages: [{ role: "user", content: [ { type: "video", video: [ "https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg" ] }, { type: "text", text: "Describe the process in this video." } ]}] }); console.log(JSON.stringify(response)); } main(); > Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. Obtain an API key. curl curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen-vl-max", "messages": [ { "role": "user", "content": [ { "type": "video", "video": [ "https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg", "https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg" ] }, { "type": "text", "text": "Describe the process in this video." } ] } ] }' > For the complete Function calling workflow code, see Function calling. Python Node.js curl python import os from openai import OpenAI client = OpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", ) tools = [ # Tool 1: Get current time (no parameters needed) { "type": "function", "function": { "name": "get_current_time", "description": "Useful when you want to know the current time.", "parameters": {} } }, # Tool 2: Get weather (requires location parameter) { "type": "function", "function": { "name": "get_current_weather", "description": "Useful when you want to check the weather in a specific city.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "A city or district, such as Beijing, Hangzhou, or Yuhang District." } }, "required": ["location"] } } } ] messages = [{"role": "user", "content": "What is the weather like in Hangzhou?"}] completion = client.chat.completions.create( model="qwen-plus", # Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages=messages, tools=tools ) print(completion.model_dump_json()) nodejs import OpenAI from "openai"; const openai = new OpenAI( { // If the environment variable is not set, replace the following line with: apiKey: "sk-xxx", apiKey: process.env.DASHSCOPE_API_KEY, baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" } ); const messages = [{"role": "user", "content": "What is the weather like in Hangzhou?"}]; const tools = [ // Tool 1: Get current time (no parameters needed) { "type": "function", "function": { "name": "get_current_time", "description": "Useful when you want to know the current time.", "parameters": {} } }, // Tool 2: Get weather (requires location parameter) { "type": "function", "function": { "name": "get_current_weather", "description": "Useful when you want to check the weather in a specific city.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "A city or district, such as Beijing, Hangzhou, or Yuhang District." } }, "required": ["location"] } } } ]; async function main() { const response = await openai.chat.completions.create({ model: "qwen-plus", // Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models messages: messages, tools: tools, }); console.log(JSON.stringify(response)); } main(); > Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. Obtain an API key. curl curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen-plus", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the weather like in Hangzhou?" } ], "tools": [ { "type": "function", "function": { "name": "get_current_time", "description": "Useful when you want to know the current time.", "parameters": {} } }, { "type": "function", "function": { "name": "get_current_weather", "description": "Useful when you want to check the weather in a specific city.", "parameters": { "type": "object", "properties": { "location":{ "type": "string", "description": "A city or district, such as Beijing, Hangzhou, or Yuhang District." } }, "required": ["location"] } } } ] }' python import os import asyncio from openai import AsyncOpenAI import platform client = AsyncOpenAI( # If the environment variable is not set, replace the following line with: api_key="sk-xxx" # If you use a model in the China (Beijing) region, you need to use an API KEY for that region. Get it here: https://bailian.console.alibabacloud.com/?tab=model#/api-key api_key=os.getenv("DASHSCOPE_API_KEY"), base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", ) async def main(): response = await client.chat.completions.create( messages=[{"role": "user", "content": "Who are you?"}], model="qwen-plus", # Model list: https://www.alibabacloud.com/help/en/model-studio/getting-started/models ) print(response.model_dump_json()) if platform.system() == "Windows": asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) asyncio.run(main())
model`string` (Required) The name of the model to use. Supported models include Qwen large language models (commercial and open source), Qwen-VL, Qwen-Coder, Qwen-Omni, and Qwen-Math, DeepSeek, Kimi, GLM, MiniMax. For specific model names and billing information, check in the console.
messages`array` (Required) The conversation history for the model, listed in chronological order. Message types System Message `object` (Optional) A system message that defines the role, tone, task objectives, or constraints for the large language model. Place it at the beginning of the `messages` array. > Do not set a System Message for the QwQ model. A System Message has no effect on the QVQ model. Properties content`string` (Required) A system instruction that defines the model’s role, behavior, response style, and task constraints. role`string` (Required) The role for a system message. The value is fixed as `system`. User Message `object` (Required) A user message that passes questions, instructions, or context to the model. Properties content`string or array` (Required) The message content. This is a string for text-only input. It is an array for multi-modal input, such as images, or if explicit caching is enabled. Properties for multi-modal models or when explicit caching is enabled type`string` (Required) Valid values: - `text` Set to `text` for text input. - `image_url` Set to `image_url` for image input. - `input_audio` Set to `input_audio` for audio input. - `video` Set to `video` when the video input is an image list. - `video_url` Set to `video_url` for video file input. > Only some Qwen-VL models accept video files as input. See Video understanding (Qwen-VL). The QVQ and Qwen-Omni models support direct video file input. text`string` The input text. This parameter is required when `type` is `text`. image_url`object` The input image information. This parameter is required when `type` is `image_url`. Properties url`string` (Required) The URL or Base64 Data URL of the image. To pass a local file, see Image and video understanding. input_audio`object` The input audio information. This parameter is required when `type` is `input_audio`. Properties data`string` (Required) The URL or Base64 Data URL of the audio. To pass a local file, see Input Base64-encoded local file. format`string` (Required) The format of the input audio, such as `mp3` or `wav`. video`array` The video information represented as an image list. This parameter is required when `type` is `video`. For usage instructions, see Video understanding (Qwen-VL), Video understanding (QVQ), or Video understanding (Qwen-Omni). Example value: `json [ "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg" ]` video_url`object` The input video file information. This parameter is required when `type` is `video_url`. Qwen-VL can only understand the visual information in video files. Qwen-Omni can understand both the visual and audio information. Properties url`string` (Required) The public network URL or Base64 Data URL of the video file. To input a local video file, see Input Base64-encoded local file. fps`float` (Optional) The number of frames to extract per second. The value must be in the range of [0.1, 10]. The default value is 2.0. > For MiniMax/MiniMax-M3, the fps range is [0.2, 5] with a default value of 1. Function description Fps provides the following features: - For video file input, it controls the frame extraction frequency. One frame is extracted every fps1 seconds. > Applicable to Qwen-VL, and QVQ models. - It informs the model of the time interval between adjacent frames. This helps the model better understand the temporal dynamics of the video. This function applies to both video file and image list inputs. It is suitable for scenarios such as event time localization or segment content summarization. > Supports the `Qwen3.7`, `Qwen3.6`, `Qwen3.5`, `Qwen3-VL`, `Qwen2.5-VL`, `Qwen3.5-Omni`, and `QVQ` models. A larger `fps` value is suitable for high-speed motion scenarios, such as sports events or action movies. A smaller `fps` value is suitable for long videos or content with static scenes. Example values - For image list input: `{"video":["https://xx1.jpg",...,"https://xxn.jpg"],"fps":2}` - For video file input: `{"video": "https://xx1.mp4","fps":2}` min_pixels`integer` (Optional) Sets the minimum pixel threshold for an input image or video frame. If an input image or video frame has a pixel count less than `min_pixels`, it is scaled up until the total pixel count exceeds `min_pixels`. Applicable to Qwen-VL and QVQ models. Value range - For image input: - `Qwen3.7`, `Qwen3.6`, `Qwen3.5`, and `Qwen3-VL`: The default and minimum value is `65536`. - `Qwen3.5-Omni`: The default and minimum value is `24576`. - `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: The default and minimum value is `4096`. - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: The default and minimum value is `3136`. - For video file or image list input: - Qwen3.7, Qwen3.6, Qwen3.5-Omni, Qwen3.5, Qwen3-VL (including commercial and open source versions), `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: The default value is `65536`. The minimum value is `4096`. - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: The default value is `50176`. The minimum value is `3136`. Example values - For image input: `{"type": "image_url","image_url": {"url":"https://xxxx.jpg"},"min_pixels": 65536}` - For video file input: `{"type": "video_url","video_url": {"url":"https://xxxx.mp4"},"min_pixels": 65536}` - For image list input: `{"type": "video","video": ["https://xx1.jpg",...,"https://xxn.jpg"],"min_pixels": 65536}` max_pixels`integer` (Optional) Sets the maximum pixel threshold for an input image or video frame. If an input image or video has a pixel count within the `[min_pixels, max_pixels]` range, the model processes the original image. If the pixel count exceeds `max_pixels`, the image is scaled down until its total pixel count is less than `max_pixels`. Applicable to Qwen-VL and QVQ models. Value range - For image input: `max_pixels` is related to whether the `vl_high_resolution_images` parameter is enabled. - If `vl_high_resolution_images` is `False`: - `Qwen3.7`,`Qwen3.6`, `Qwen3.5`, and `Qwen3-VL`: The default value is `2621440`. The maximum value is `16777216`. - `Qwen3.5-Omni`: The default value is `1310720`. The maximum value is `16777216`. - `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: The default value is `1310720`. The maximum value is `16777216`. - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: The default value is `1003520`. The maximum value is `12845056`. - If `vl_high_resolution_images` is `True`: - `Qwen3.7`,`Qwen3.6`, `Qwen3.5-Omni`, `Qwen3.5`, `Qwen3-VL`, `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: `max_pixels` is invalid. The maximum pixel count for an input image is fixed at `16777216`. - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: `max_pixels` is invalid. The maximum pixel count for an input image is fixed at `12845056`. - For video file or image list input: - `Qwen3.7 series`, `Qwen3.6 series`, `Qwen3.5-Omni`, `Qwen3.5` series, close-source `Qwen3-VL` series, `qwen3-vl-235b-a22b-thinking`, and `qwen3-vl-235b-a22b-instruct`: The default value is `655360`. The maximum value is `2048000`. - Other `Qwen3-VL` open source models, `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: The default value is `655360`. The maximum value is `786432`. - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: The default value is `501760`. The maximum value is `602112`. Example values - For image input: `{"type": "image_url","image_url": {"url":"https://xxxx.jpg"},"max_pixels": 8388608}` - For video file input: `{"type": "video_url","video_url": {"url":"https://xxxx.mp4"},"max_pixels": 655360}` - For image list input: `{"type": "video","video": ["https://xx1.jpg",...,"https://xxn.jpg"],"max_pixels": 655360}` total_pixels`integer` (Optional) Limits the total number of pixels for all frames extracted from a video (pixels per frame × total number of frames). If the video’s total pixel count exceeds this limit, the system scales down the video frames. However, it ensures that the pixel count of any single frame remains within the `[min_pixels, max_pixels]` range. Applicable to Qwen-VL and QVQ models. For long videos with many extracted frames, lower this value to reduce token consumption and processing time. However, this may cause a loss of image detail. Value range - `Qwen3.7` series, `Qwen3.6` series and `Qwen3.5` series: The default and maximum value is `819200000`. This value corresponds to `800000` image tokens (1 image token per 32×32 pixels). - `Qwen3.5-Omni`: The default and maximum value is `184549376`. This value corresponds to `180224` image tokens (1 image token per 32×32 pixels). - Close-source `Qwen3-VL` series, `qwen3-vl-235b-a22b-thinking`, and `qwen3-vl-235b-a22b-instruct`: The default and maximum value is `134217728`. This value corresponds to `131072` image tokens (1 image token per 32×32 pixels). - Other `Qwen3-VL` open source models, `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: The default and minimum value is `67108864`. This value corresponds to `65536` image tokens (1 image token per 32×32 pixels). - Other `qwen-vl-plus` models, other `qwen-vl-max` models, open source `Qwen2.5-VL` series, and `QVQ` series models: The default and minimum value is `51380224`. This value corresponds to `65536` image tokens (1 image token per 28×28 pixels). Example values - For video file input: `{"type": "video_url","video_url": {"url":"https://xxxx.mp4"},"total_pixels": 134217728}` - For image list input: `{"type": "video","video": ["https://xx1.jpg",...,"https://xxn.jpg"],"total_pixels": 134217728}` cache_control`object` (Optional) Enables explicit caching. See Explicit cache. Properties type`string` (Required) Only `ephemeral` is supported. role`string` (Required) The role for a user message. The value is fixed as `user`. Assistant Message `object` (Optional) The model’s reply. This is typically sent back to the model as context in a multi-turn conversation. Properties content`string` (Optional) The text content of the model’s reply. If `tool_calls` is included, `content` can be empty. Otherwise, `content` is required. role`string` (Required) The role for an assistant message. The value is fixed as `assistant`. partial`boolean` (Optional) Defaults to: `false` Specifies whether to enable partial mode. Valid values: - true - false See partial mode. tool_calls`array` (Optional) The tool and input parameter information returned after a function call is initiated. It contains one or more objects and is retrieved from the `tool_calls` field of the previous model response. Properties id`string` (Required) The ID of the tool response. type`string` (Required) The tool type. Currently, only `function` is supported. function`object` (Required) The tool and input parameter information. Properties name`string` (Required) The tool name. arguments`string` (Required) The input parameter information, formatted as a JSON string. index`integer` (Required) The index of the current tool information in the `tool_calls` array. Tool Message `object` (Optional) The output information of the tool. Properties content`string` (Required) The output content of the tool function. It must be a string. If the tool returns structured data, such as JSON, serialize it into a string. role`string` (Required) The value is fixed as `tool`. tool_call_id`string` (Required) The ID returned after a function call is initiated. Retrieve it from completion.choices[0].message.tool_calls[$index].id. This ID marks the tool corresponding to the Tool Message.
stream`boolean` (Optional. Defaults to `false`.) Enables streaming output mode. See Streaming output. Valid values: - `false`: The model returns the complete content at once after generation. - `true`: The model generates and sends output incrementally. A data block (chunk) is returned as soon as part of the content is generated. You can read these chunks in real time to assemble the full reply. You can set this parameter to `true` to improve the reading experience and reduce the risk of timeouts. Note For non-streaming calls, if the request does not complete within 300 seconds, the service will interrupt the request and return the content generated so far (instead of an error). For scenarios with long outputs, use streaming calls. For more information, see the timeout section in Text generation model overview.
stream_options`object`(Optional) Configuration options for streaming output. This parameter is effective only when `stream` is set to `true`. Properties include_usage`boolean`(Optional. Defaults to `false`) Specifies whether to include token consumption information in the last data block of the response. Valid values: - `true` - `false` > For streaming output, token consumption information is included only in the last data block of the response.
modalities`array`(Optional) Defaults to: `["text"]` Specifies the modalities of the output data. This parameter applies only to Qwen-Omni models. See Non-real-time (Qwen-Omni). Valid values: - `["text","audio"]` - `["text"]`
audio`object` (Optional) The voice and format of the output audio. This parameter applies only to Qwen-Omni models, and you must set the `modalities` parameter to `["text","audio"]`. See Non-real-time (Qwen-Omni). Properties voice`string` (Required) The voice used for the output audio. See Non-real-time (Qwen-Omni). format`string` (Required) The format of the output audio. Only `wav` is supported.
temperature`float` (Optional) The sampling temperature controls the diversity of the generated text. Higher values increase diversity, while lower values make the output more deterministic. The value must be greater than or equal to 0 and less than 2. Both the temperature and top_p parameters control the diversity of the generated text. Set only one of them. See Overview. > Do not modify the default temperature value for QVQ models.
top_p`float` (Optional) The probability threshold for nucleus sampling. This parameter controls the diversity of the text that the model generates. A higher `top_p` value produces more diverse text. A lower `top_p` value produces more deterministic text. Value range: (0, 1.0] Both `temperature` and `top_p` control the diversity of the generated text. Set only one of these parameters. See Overview. > Do not modify the default `top_p` value for QVQ models.
top_k`integer` (Optional) Specifies the number of candidate tokens to use for sampling during generation. A larger value produces more random output, whereas a smaller value produces more deterministic output. If set to `null` or a value greater than 100, the `top_k` strategy is disabled and only the `top_p` strategy takes effect. The value must be an integer greater than or equal to 0. Default top_k values QVQ series, and : 10. QwQ series: 40. Other qwen-vl-plus series, models before qwen2.5-omni-7b: 1. Qwen3-Omni-Flash series: 50. All other models: 20. GLM series (Alibaba Cloud direct): 20; DeepSeek/Kimi/MiniMax series do not support the top_k parameter. > This parameter is not a standard OpenAI parameter. When using the Python SDK, include it in the extra_body object. Configure it as follows: extra_body={"top_k":xxx}. > You must not change the default top_k value for QVQ models.
repetition_penalty`float` (Optional) Controls the repetitiveness of consecutive sequences during generation. A higher repetition_penalty reduces repetition, while 1.0 means no penalty. The value must be greater than 0, with no strict upper limit. > This parameter is not a standard OpenAI parameter. When using the Python SDK, include it in the extra_body object. Configure it as follows: extra_body={"repetition_penalty":xxx}. > When using the qwen-vl-plus_2025-01-25 model for text extraction, we recommend setting repetition_penalty to 1.0. > Do not modify the default repetition_penalty value for QVQ models.
presence_penalty`float` (Optional) Controls how strongly the model avoids repeating content. Valid values: -2.0 to 2.0. Positive values reduce repetition. Negative values increase it. For scenarios that require diversity and creativity, such as creative writing or brainstorming, increase this value. For scenarios that require consistency and terminological accuracy, such as technical documents or formal text, decrease this value. Default presence_penalty values Qwen3.7 (non-thinking mode), Qwen3.6 (non-thinking mode), Qwen3.5-Omni, Qwen3.5 (non-thinking mode), qwen3-max-preview (thinking mode), Qwen3 (non-thinking mode), Qwen3-Instruct series/1.7b/4b (thinking mode), QVQ series, qwen-max, qwen2.5-vl series, qwen-vl-max series, qwen-vl-plus, Qwen3-VL (non-thinking): 1.5. qwen3-8b/14b/32b/30b-a3b/235b-a22b (thinking mode), qwen-plus/qwen-plus-latest/2025-04-28 (thinking mode), qwen-turbo/qwen-turbo/2025-04-28 (thinking mode): 0.5. All other models: 0.0. DeepSeek series (Alibaba Cloud direct): deepseek-r1, deepseek-r1-0528, deepseek-r1-distill-qwen distill series: 1; Kimi series (Alibaba Cloud direct): kimi-k2.6, kimi-k2.5: 0.0; Kimi series (Moonshot AI direct): 0.0; MiniMax series (Alibaba Cloud direct): MiniMax-M2.5, MiniMax-M2.1: 0.0; Other DeepSeek/Kimi/GLM/MiniMax models have no default value. How it works When the parameter value is positive, the model penalizes tokens that already appear in the generated text. The penalty does not depend on how many times a token appears. This reduces the likelihood of those tokens reappearing, which decreases repetition and increases lexical diversity. Example Prompt: Translate this sentence into English: "Esta película es buena. La trama es buena, la actuación es buena, la música es buena, y en general, toda la película es simplemente buena. Es realmente buena, de hecho. La trama es tan buena, y la actuación es tan buena, y la música es tan buena." Parameter value 2.0: This movie is very good. The plot is great, the acting is great, the music is also very good, and overall, the whole movie is incredibly good. In fact, it is truly excellent. The plot is very exciting, the acting is outstanding, and the music is so beautiful. Parameter value 0.0: This movie is good. The plot is good, the acting is good, the music is also good, and overall, the whole movie is very good. In fact, it is really great. The plot is very good, the acting is also very outstanding, and the music is also excellent. Parameter value -2.0: This movie is very good. The plot is very good, the acting is very good, the music is also very good, and overall, the whole movie is very good. In fact, it is really great. The plot is very good, the acting is also very good, and the music is also very good. > When using the qwen-vl-plus model for text extraction, set presence_penalty to 1.5. > Do not modify the default presence_penalty value for QVQ models.
response_format`object` (Optional. Defaults to `{"type": "text"}`) The format of the response content. Valid values: - `{"type": "text"}`: Returns a plain text response. - `{"type": "json_object"}`: Returns a JSON string that conforms to standard JSON syntax. > See Structured output. > If you specify `{"type": "json_object"}`, explicitly instruct the model to output JSON in the prompt, such as by adding “Please output in JSON format.” Otherwise, an error occurs. > For supported models, see Structured output. Properties type`string` (Required) The format of the response content. Valid values: - `text`: Returns a plain text response. - `json_object`: Returns a JSON string that conforms to standard JSON syntax.
max_tokens`integer` (Optional, Deprecated) > This parameter is being deprecated. Use `max_completion_tokens` for new integrations. The maximum number of tokens in the response. Generation stops when this limit is reached, and the `finish_reason` field in the response is set to `length`. The default and maximum values correspond to the model’s maximum output length. Check in the console. You can use this parameter to control output length in scenarios such as generating summaries or keywords, or to reduce costs and shorten response time. When `max_tokens` is triggered, the `finish_reason` field in the response is set to `length`. > `max_tokens` does not limit the length of the chain-of-thought.
max_completion_tokens`integer` (Optional) The maximum number of tokens in this response, including the chain-of-thought tokens. Generation stops when this limit is reached, and the `finish_reason` field in the response is set to `length`. The default and maximum values correspond to the model's maximum output length. Check in the console. Difference from `max_tokens`: `max_completion_tokens` limits the total length of both the chain-of-thought and the final response, while `max_tokens` does not limit the chain-of-thought length. For reasoning models, `max_completion_tokens` is recommended. Supported models: - Qwen-Max: Qwen3.7-Max and later - Qwen-Plus: Qwen3.5-Plus and later - Qwen-Flash: Qwen3.5-Flash and later - Kimi: kimi-k2.5 and later - GLM: glm-5 and later - MiniMax: MiniMax-M2.5 and later - DeepSeek: deepseek-v3, deepseek-r1, deepseek-r1-0528, deepseek-v3.1, deepseek-v3.2, deepseek-v3.2-exp, deepseek-v4-pro, deepseek-v4-flash and later > Third-party direct-sale models are not included. > The actual number of output tokens may differ from the configured `max_completion_tokens` value by up to 10 tokens.
vl_high_resolution_images`boolean` (Optional. Defaults to `false`.) Increases the maximum pixel limit for input images to the pixel value corresponding to 16384 tokens. See Processing high-resolution images. - `vl_high_resolution_images: true`: Uses a fixed-resolution strategy and ignores the `max_pixels` setting. If an image exceeds this resolution, its total pixel count is downscaled to meet the limit. Click to view the pixel limits for each model When `vl_high_resolution_images` is `true`, different models have different pixel limits: - Qwen3.7 series, `Qwen3.6` series, `Qwen3.5` series, `Qwen3-VL` series, `qwen-vl-max`, `qwen-vl-max-0813`, `qwen-vl-plus`, `qwen-vl-plus-0815`: `16,777,216` (each `Token` corresponds to `32×32` pixels, i.e., `16,384×32×32`) - `QVQ series` and other `Qwen2.5-VL series` models: `12,845,056` (each `Token` corresponds to `28×28` pixels, i.e., `16,384×28×28`) - If `vl_high_resolution_images` is `false`, the actual pixel limit is determined by `max_pixels`. If an input image exceeds `max_pixels`, it is downscaled to fit within `max_pixels`. The default pixel limits for models match the default value of `max_pixels`. > This parameter is not a standard OpenAI parameter. When making calls with the Python SDK, include this parameter in the extra_body object. The configuration is as follows: extra_body={"vl_high_resolution_images":xxx}.
n`integer` (Optional. Defaults to 1) The number of responses to generate must be an integer in the range of `1-4`. This is useful for scenarios that require multiple candidate responses, such as creative writing or ad copy. > This parameter is supported only by the Qwen3 (non-thinking mode) models. > If you pass the `tools` parameter, set `n` to 1. > Increasing n increases output token consumption but does not affect input token consumption.
enable_thinking`boolean` (Optional) Enables the thinking mode for hybrid thinking models. This mode is available for the Qwen3.7, Qwen3.6, Qwen3.5, Qwen3, Qwen3-Omni-Flash, and Qwen3-VL models, as well as the DeepSeek-V4-Pro/V4-Flash series, DeepSeek-V3.2/V3.2-exp/V3.1 series, Kimi-K2.6/K2.5 series, and GLM series. The DeepSeek-V4 series defaults to thinking mode. You can use the `reasoning_effort` parameter to adjust reasoning intensity. Valid values: - `true` > When enabled, the thinking content is returned in the `reasoning_content` field. - `false` Default values for different models: Supported models > This parameter is not a standard OpenAI parameter. When you make a call using the Python SDK, place it in the extra_body object. It is configured as follows: `extra_body={"enable_thinking": xxx}`. > MiniMax/MiniMax-M3 (Xiyu Technology direct) does not use this parameter. Use the `thinking` parameter instead.
thinking`object` (Optional) Defaults to: `{"type":"adaptive"}` Controls the thinking mode for MiniMax/MiniMax-M3 (Xiyu Technology direct). `thinking.type` options: - `adaptive`: Adaptive (default). The model autonomously determines whether thinking is needed. - `disabled`: Disables thinking and responds directly. > This parameter is not a standard OpenAI parameter. When you make a call using the Python SDK, place it in the extra_body object. It is configured as follows: `extra_body={"thinking": {"type": "adaptive"}}`.
preserve_thinking`boolean` (Optional) Defaults to: `false` Controls whether to add the reasoning_content from past assistant messages to the model input. Use this when the model needs to refer to its past reasoning. This parameter currently supports qwen3.7-max, qwen3.7-max-2026-05-20 and later snapshot versions, qwen3.7-max-preview, qwen3.7-max-2026-05-17, qwen3.6-max-preview, qwen3.7-plus, qwen3.7-plus-2026-05-26, qwen3.6-plus, qwen3.6-plus-2026-04-02, and kimi-k2.6 (deployed on Alibaba Cloud Model Studio). - If past messages lack reasoning_content, enabling this parameter does not cause an error. - When enabled, reasoning_content is included in the input tokens and is billed. > This parameter is not a standard OpenAI parameter. When using the Python SDK, include it in the extra_body object. Configure this parameter as follows: `extra_body={"preserve_thinking": True}`.
thinking_budget`integer` (Optional) The maximum number of tokens for the thinking process. This applies to Qwen3.7, Qwen3.6, Qwen3.5, Qwen3-VL, and the commercial and open source versions of Qwen3 models. See Limit thinking length. The default value is the model's maximum chain-of-thought length. Check in the console. > This parameter is not a standard OpenAI parameter. When you use the Python SDK, place this parameter in the extra_body object. Configure the parameter as follows: `extra_body={"thinking_budget": xxx}`.
reasoning_effort`string` (Optional) Defaults to: `high` Controls the reasoning intensity for DeepSeek-V4 series models. Valid values: - `high`: High-intensity reasoning - `max`: Maximum-intensity reasoning low and medium map to high, and xhigh maps to max. Available for deepseek-v4-pro and deepseek-v4-flash. > This parameter is not a standard OpenAI parameter. When using the Python SDK, include it in the extra_body object. Configure the parameter as follows: `extra_body={"reasoning_effort": "high"}`.
tool_stream`boolean` (Optional) Defaults to: `false` Only takes effect when `stream=true`. Currently only supported by Qwen and GLM series. Qwen series supported models: - qwen-max series: text modality of qwen3.7-max series - qwen-plus series: text modality of qwen3.7-plus series and qwen3.6-plus series, and all modalities of qwen3.5-plus series - qwen-flash series: all modalities of qwen3.6-flash series and qwen3.5-flash series Qwen series usage reference: tool_stream only affects complex tool arguments. Simple tool arguments are streamed as long as `stream=true` is enabled. A complex tool refers to a tool whose definition contains parameters of type array or object. - `tool_stream=false`: Complex tool arguments are output all at once. This is the default behavior, which produces more accurate results for complex formats. - `tool_stream=true`: Complex tool arguments are output in a streaming manner, which eliminates timeout risks for complex formats. GLM series supported models: glm-4.6, glm-4.7, glm-5, and glm-5.1. GLM series usage reference: - `tool_stream=false`: Tool arguments are output all at once. This is the default behavior, which produces more accurate results for complex formats. - `tool_stream=true`: Tool arguments are output in a streaming manner, which eliminates timeout risks for complex formats. > This parameter is not a standard OpenAI parameter. When using the Python SDK, include it in the extra_body object. Configure the parameter as follows: `extra_body={"tool_stream": true}`.
enable_code_interpreter`boolean` (Optional. Defaults to `false`.) Specifies whether to enable the code interpreter feature. See Code Interpreter. Valid values: - `true` - `false` > This parameter is not a standard OpenAI parameter. When you make calls using the Python SDK, include this parameter in the extra_body object. The configuration is as follows: `extra_body={"enable_code_interpreter": xxx}`.
seed`integer` (Optional) The random number seed. This parameter ensures that results are reproducible. If you use the same `seed` value in a call and the other parameters remain unchanged, the model returns the same result whenever possible. Valid values: `[0,231-1]`.
logprobs`boolean` (Optional) Defaults to `false` Specifies whether to return the log probabilities of the output tokens. Valid values: - `true` - `false` > Content generated during the thinking phase (`reasoning_content`) does not include log probabilities. Supported models - Qwen-plus series snapshots (excluding the stable model) - Qwen-turbo series snapshots (excluding the stable model) - Qwen3-vl-plus models (including the stable model) - Qwen3-vl-flash models (including the stable model) - Qwen3 open source models
top_logprobs`integer` (Optional. Defaults to 0) You can specify the number of most likely candidate tokens to return at each generation step. Valid values: 0 to 5 This parameter applies only if `logprobs` is set to `true`.
stop`string or array` (Optional) This parameter specifies stop words. If a string or `token_id` specified in `stop` appears in the text generated by the model, generation stops immediately. Pass sensitive words to control the model's output. > If stop is an array, do not use a `token_id` or a string as elements simultaneously. For example, `["Hello",104307]` is not a valid value.
tools`array`(Optional) An array of one or more tool objects that the model can call in function calling. See Function calling. When tools is set and the model determines that a tool needs to be called, the response returns tool information in the tool_calls field. Properties type`string` (Required) Tool type. Currently supports only `function`. function`object` (Required) Properties name`string` (Required) The tool name. It must contain only letters, digits, underscores (`_`), and hyphens (`-`). The name can be up to 64 tokens long. description`string` (Required) A description of the tool. This helps the model determine when and how to call the tool. parameters`object`(Optional) Defaults to `{}` The tool's parameters must be described using a valid JSON Schema. For more information about JSON Schema, see this link. If the `parameters` parameter is empty, this indicates that the tool has no input parameters, such as a time query tool. > To improve the accuracy of tool calling, we recommend passing `parameters`.
tool_choice`string or object` (Optional. Defaults to `auto`.) The tool selection policy. Use this parameter to force a tool call for certain types of questions, such as always using a specific tool or disabling all tools. Valid values: - `auto` The model automatically selects a tool. - `none` To disable tool calling, set the `tool_choice` parameter to `none`. - `{"type": "function", "function": {"name": "the_function_to_call"}}` To force a call to a specific tool, set the `tool_choice` parameter to `{"type": "function", "function": {"name": "the_function_to_call"}}`, where `the_function_to_call` is the name of the specified tool function. > Models in thinking mode do not support forcing a call to a specific tool.
parallel_tool_calls`boolean` (Optional. Defaults to `false`.) Specifies whether to enable parallel tool calling. See Parallel tool calling. Valid values: - `true` - `false`
enable_search`boolean` (Optional) Defaults to: `false` Enables web search. See Web search. Valid values: - `true` > If web search is not triggered after enabling this parameter, optimize the prompt or set the `forced_search` parameter in `search_options` to enable forced search. - `false` > Enabling the web search feature may increase token consumption. > This parameter is not a standard OpenAI parameter. When calling with the Python SDK, include it in the extra_body object. Configure it as follows: `extra_body={"enable_search": True}`.
search_options`object` (Optional) The web search strategy. See Web search. Properties forced_search`boolean` (Optional) Defaults to: `false` Forces web search. This parameter takes effect only when `enable_search` is `true`. Valid values: - true: Forcefully enables the feature. - false: Lets the model decide whether to perform a web search. search_strategy`string` (Optional) Defaults to: `turbo` The search scale strategy. This parameter takes effect only when `enable_search` is `true`. Valid values: - `turbo` (default): Balances response speed and search effectiveness. Suitable for most scenarios. - `max`: Uses a more comprehensive search strategy and calls multiple search engines to retrieve more detailed results. Response time may be longer. - `agent`: Calls the web search tool and the model multiple times to retrieve and integrate information across multiple rounds. > Applicable only to qwen3.5-plus, qwen3.5-plus-2026-02-15, qwen3.5-flash, qwen3.5-flash-2026-02-23, qwen3-max, qwen3-max-2026-01-23, qwen3-max-2025-09-23, qwen3.5-omni-plus, qwen3.5-omni-plus-2026-03-15, qwen3.5-omni-flash, and qwen3.5-omni-flash-2026-03-15. - `agent_max`: Adds web extraction support to the `agent` strategy. See Web extractor. > Applicable only to the thinking mode of qwen3-max, and qwen3-max-2026-01-23. enable_search_extension`boolean` (Optional) Defaults to: `false` Enables domain-specific search. This parameter takes effect only when `enable_search` is `true`. Valid values: - `true` - `false` > This parameter is not a standard OpenAI parameter. When you call it using the Python SDK, include it in the extra_body object.Configure it as follows: `extra_body={"search_options": xxx}`.


## Chat response object (non-streaming output)	json { "choices": [ { "message": { "role": "assistant", "content": "I am a large-scale language model developed by Alibaba Cloud. My name is Qwen." }, "finish_reason": "stop", "index": 0, "logprobs": null } ], "object": "chat.completion", "usage": { "prompt_tokens": 3019, "completion_tokens": 104, "total_tokens": 3123, "prompt_tokens_details": { "cached_tokens": 2048 } }, "created": 1735120033, "system_fingerprint": null, "model": "qwen-plus", "id": "chatcmpl-6ada9ed2-7f33-9de2-8bb0-78bd4035025a" }
id`string` The unique identifier for this request.
choices`array` An array of generated content from the model. Properties finish_reason`string` The reason the model stopped generating output. The following scenarios apply: - `stop`: The model stopped naturally or because the `stop` input parameter was triggered. - `length`: The generation stopped because the output reached the maximum length. - `tool_calls`: The model stopped to call a tool. index`integer` The index of this object in the `choices` array. logprobs`object` Log probability information for tokens in the model's output. Properties content`array` An array of tokens and their corresponding log probabilities. Properties token`string` The text of the current token. bytes`array` A list of raw UTF-8 bytes for the current token. This list enables accurate reconstruction of output content, such as emojis or Chinese characters. logprob`float` The log probability of the current token. A return value of `null` indicates an extremely low probability. top_logprobs`array` The most likely candidate tokens for the current token position. The number of candidates matches the `top_logprobs` request parameter. Each element contains: Properties token`string` The candidate token text. bytes`array` A list of raw UTF-8 bytes for the current token. This list enables accurate reconstruction of output content, such as emojis or Chinese characters. logprob`float` The log probability of this candidate token. A return value of null indicates an extremely low probability. message`object` The message generated by the model. Properties content`string` The content of the model's response. reasoning_content`string` The content of the model's chain-of-thought reasoning. refusal`string` This field is always `null`. role`string` The role of the message. The value is always `assistant`. audio`object` This field is always `null`. function_call (to be deprecated) `object` This field is always `null`. For function calls, use the `tool_calls` parameter instead. tool_calls`array` Information about tools and their input parameters that the model generates after initiating a function call. Properties id`string` The unique identifier for this tool response. type`string` The type of the tool. Currently, only `function` is supported. function`object` Information about the tool. Properties name`string` The name of the tool. arguments`string` The input parameters, formatted as a JSON string. > Model outputs are non-deterministic. The output parameters might not match the function signature. Validate the parameters before calling the function. index`integer` The index of this tool in the `tool_calls` array.
created`integer` The Unix timestamp, in seconds, when the request was created.
model`string` The model used for this request.
object`string` The value is always `chat.completion`.
service_tier`string` This field is currently fixed as `null`.
system_fingerprint`string` This field is currently fixed as `null`.
usage`object` Token consumption details for this request. Properties completion_tokens`integer` The number of tokens in the model's output. prompt_tokens`integer` The number of tokens in the input. Additional Notes total_tokens`integer` The total number of tokens consumed, equal to the sum of `prompt_tokens` and `completion_tokens`. completion_tokens_details`object` A fine-grained breakdown of output tokens. Properties audio_tokens`integer` This field is currently set to `null`. reasoning_tokens`integer` This field is currently set to `null`. text_tokens`integer` The number of text tokens in the output. prompt_tokens_details`object` A fine-grained breakdown of input tokens. Properties audio_tokens`integer` This field is currently set to `null`. cached_tokens`integer` The number of tokens that hit the cache. See context cache. text_tokens`integer` The number of text tokens in the input. image_tokens`integer` The number of image tokens in the input. video_tokens`integer` The number of tokens for the input video file or image list. cache_creation`object` Information about the creation of an explicit cache. Properties ephemeral_5m_input_tokens`integer` The number of tokens used to create the explicit cache. cache_creation_input_tokens`integer` The number of tokens used to create the explicit cache. cache_type`string` When you use an explicit cache, the value is `ephemeral`. Otherwise, this field does not exist.


## Chat response chunk object (streaming output)	json {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"I am a ","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"large-scale ","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"language model ","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"from Alibaba Cloud. My name ","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"is Qwen","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":".","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[],"created":1735113344,"model":"qwen-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":{"completion_tokens":17,"prompt_tokens":22,"total_tokens":39,"completion_tokens_details":null,"prompt_tokens_details":{"audio_tokens":null,"cached_tokens":0}}}
id`string` The unique identifier for this request. All chunks in the same response share the same id.
choices`array` An array of generated content objects. This array contains one or more objects. If you set the `include_usage` parameter to `true`, the `choices` array is empty in the final chunk. Properties delta`object` The incremental content object for this chunk. Properties content`string` The incremental message content. reasoning_content`string` The incremental chain-of-thought content. function_call`object` This value defaults to `null`. See the `tool_calls` parameter. audio`object` The response generated by the Non-real-time (Qwen-Omni) model. Properties data`string` The incremental Base64-encoded audio data. expires_at`integer` The UNIX timestamp when the request was created. refusal`object` This parameter is always `null`. role`string` The role of the incremental message object. This property appears only in the first chunk. tool_calls`array` Information about tools and input parameters that the model generates after a function call. Properties index`integer` The index of the current tool in the `tool_calls` array. id`string` The unique ID for this tool response. function`object` Information about the invoked tool. Properties arguments`string` Incremental information about the input parameters. Concatenate the `arguments` from all chunks to obtain the complete input parameters. > Because large model responses have a degree of randomness, the output parameter information may not conform to the function signature. Validate the parameters before you call the function. name`string` The tool name. This property appears only in the first chunk. type`string` The tool type. Currently, only `function` is supported. finish_reason`string` The model stops generating for one of the following reasons: - `stop`: The `stop` input parameter was triggered or output stopped naturally. - Before generation is complete, it is `null`. - `length`: The maximum output length was reached. - `tool_calls`: The model stopped to make a tool call. index`integer` The index of the current response in the `choices` array. If the input parameter n is greater than 1, use this parameter to reconstruct the complete content for each response. logprobs`object` Probability information for the current object. Properties content`array` An array of tokens with associated log probability information. Properties token`string` The current token. bytes`array` A list of the raw UTF-8 bytes of the current token. This is used to accurately restore the output content and is helpful when you handle emojis and Chinese characters. logprob`float` The log probability of the current token. A return value of null indicates an extremely low probability. top_logprobs`array` The most likely tokens at the current position and their log probabilities. The number of elements matches the `top_logprobs` input parameter. Properties token`string` The current token. bytes`array` A list of the raw UTF-8 bytes of the current token. This is used to accurately restore the output content and is helpful when you handle emojis and Chinese characters. logprob`float` The log probability of the current token. A return value of null indicates an extremely low probability.
created`integer` The UNIX timestamp when this request was created. All chunks in the same response share the same created timestamp.
model`string` The model used for this request.
object`string` The value is always `chat.completion.chunk`.
service_tier`string` This parameter is currently fixed as `null`.
system_fingerprint`string` This parameter is currently fixed as `null`.
usage`object` Token usage statistics for this request. This object appears only in the final chunk when `include_usage` is `true`. Properties completion_tokens`integer` The number of tokens in the model's output. prompt_tokens`integer` The number of input tokens. total_tokens`integer` The total number of tokens, equal to the sum of `prompt_tokens` and `completion_tokens`. completion_tokens_details`object` Detailed breakdown of output tokens. Properties audio_tokens`integer` The number of audio tokens in the output. reasoning_tokens`integer` The number of tokens in the thinking process. text_tokens`integer` The number of output text tokens. prompt_tokens_details`object` A fine-grained breakdown of input tokens. Properties audio_tokens`integer` The number of tokens in the input audio. > The number of audio tokens in a video file is returned in this parameter. text_tokens`integer` The number of tokens in the input text. video_tokens`integer` The number of tokens for the input video, which can be an image list or a video file. image_tokens`integer` The number of tokens in the input image. cached_tokens`integer` The number of tokens that hit the cache. See Context cache. cache_creation`object` Information about the creation of an explicit cache. Properties ephemeral_5m_input_tokens`integer` The number of tokens used to create the explicit cache. cache_creation_input_tokens`integer` The number of tokens used to create the explicit cache. cache_type`string` The cache type. The value is fixed as `ephemeral`.

Error codes

If your API call fails and returns an error message, see Error codes for troubleshooting.

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Overview & Tools

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