DeepSeek - QwenCloud

This guide shows how to call DeepSeek models via the OpenAI-compatible API or DashScope SDK.

The models deepseek-v3, deepseek-v3.1, deepseek-v3.2, deepseek-v3.2-exp, deepseek-r1, deepseek-r1-0528, and deepseek-r1-distill-qwen-7b/14b/32b will be deprecated on October 10, 2026. Migrate to qwen3.7-plus, qwen3.8-max, qwen3.8-flash, qwen3.7-max, or qwen3.6-flash.

Quick start

deepseek-v4-pro-0813 is the flagship model in the DeepSeek series with 1.6T total parameters and 49B activated parameters. It natively supports context windows of up to 1 million tokens and delivers top-tier performance across coding, math, and general tasks. You can use the enable_thinking parameter to switch between thinking and non-thinking modes. The following example calls deepseek-v4-pro-0813 in thinking mode. The newest addition, deepseek-v4.1-flash, is a lightweight flagship with 552B total parameters and only 8B activated for input. It also accepts image input. Calling it works the same way as the examples below, except that its DashScope endpoint differs — see Multimodal examples. Before you begin, get an API key and set it as an environment variable. If you call the model through an SDK, install the OpenAI or DashScope SDK.

OpenAI compatible
Anthropic compatible
DashScope

The enable_thinking parameter is not part of the standard OpenAI API. In the OpenAI Python SDK, pass it through extra_body. In the Node.js SDK, pass it as a top-level parameter. The reasoning_effort parameter is a standard OpenAI parameter that you can pass directly as a top-level parameter.

Python
Node.js
curl

Example code

from openai import OpenAI
import os

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you?"}]
completion = client.chat.completions.create(
  model="deepseek-v4-pro-0813",
  messages=messages,
  extra_body={"enable_thinking": True},
  stream=True,
  stream_options={"include_usage": True},
)

reasoning_content = ""
answer_content = ""
is_answering = False
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")

for chunk in completion:
  if not chunk.choices:
    print("\n" + "=" * 20 + "Token usage" + "=" * 20 + "\n")
    print(chunk.usage)
    continue

  delta = chunk.choices[0].delta

  if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
    if not is_answering:
      print(delta.reasoning_content, end="", flush=True)
    reasoning_content += delta.reasoning_content

  if hasattr(delta, "content") and delta.content:
    if not is_answering:
      print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
      is_answering = True
    print(delta.content, end="", flush=True)
    answer_content += delta.content

Example code

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: 'https://maas.qwencloudapi.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;

async function main() {
  const messages = [{ role: 'user', content: 'Who are you?' }];

  const stream = await openai.chat.completions.create({
    model: 'deepseek-v4-pro-0813',
    messages,
    enable_thinking: true,
    stream: true,
    stream_options: { include_usage: true },
  });

  console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.repeat(20) + '\n');

  for await (const chunk of stream) {
    if (!chunk.choices?.length) {
      console.log('\n' + '='.repeat(20) + 'Token usage' + '='.repeat(20) + '\n');
      console.log(chunk.usage);
      continue;
    }

    const delta = chunk.choices[0].delta;

    if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
      if (!isAnswering) {
        process.stdout.write(delta.reasoning_content);
      }
      reasoningContent += delta.reasoning_content;
    }

    if (delta.content !== undefined && delta.content) {
      if (!isAnswering) {
        console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
        isAnswering = true;
      }
      process.stdout.write(delta.content);
      answerContent += delta.content;
    }
  }
}

main();

Example code

curl -X POST https://maas.qwencloudapi.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "deepseek-v4-pro-0813",
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true
}'

Python
curl

Example code

import anthropic
import os

client = anthropic.Anthropic(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://maas.qwencloudapi.com/apps/anthropic",
)

message = client.messages.create(
  model="deepseek-v4-pro-0813",
  max_tokens=1024,
  messages=[
    {"role": "user", "content": "Who are you?"}
  ],
  stream=True,
)

for event in message:
  if event.type == "content_block_delta":
    if hasattr(event.delta, "thinking"):
      print(event.delta.thinking, end="", flush=True)
    if hasattr(event.delta, "text"):
      print(event.delta.text, end="", flush=True)

Example code

curl -X POST https://maas.qwencloudapi.com/apps/anthropic/v1/messages \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "anthropic-version: 2023-06-01" \
-d '{
    "model": "deepseek-v4-pro-0813",
    "max_tokens": 1024,
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ]
}'

The DashScope examples below use the text-generation endpoint. deepseek-v4.1-flash is a multimodal model and is only available on the DashScope multimodal-generation endpoint, which requires MultiModalConversation — see Multimodal examples.

Python
Java
curl

Example code

import os
from dashscope import Generation

messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  model="deepseek-v4-pro-0813",
  messages=messages,
  result_format="message",
  enable_thinking=True,
  stream=True,
  incremental_output=True,
)

reasoning_content = ""
answer_content = ""
is_answering = False

print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")

for chunk in completion:
  message = chunk.output.choices[0].message
  if "reasoning_content" in message:
    if not is_answering:
      print(message.reasoning_content, end="", flush=True)
    reasoning_content += message.reasoning_content

  if message.content:
    if not is_answering:
      print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
      is_answering = True
    print(message.content, end="", flush=True)
    answer_content += message.content

print("\n" + "=" * 20 + "Token usage" + "=" * 20 + "\n")
print(chunk.usage)

Example code

DashScope Java SDK version must be 2.19.4 or later.

import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.Arrays;

public class Main {
  private static StringBuilder reasoningContent = new StringBuilder();
  private static StringBuilder finalContent = new StringBuilder();
  private static boolean isFirstPrint = true;
  private static void handleGenerationResult(GenerationResult message) {
    String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
    String content = message.getOutput().getChoices().get(0).getMessage().getContent();
    if (reasoning != null && !reasoning.isEmpty()) {
      reasoningContent.append(reasoning);
      if (isFirstPrint) {
        System.out.println("====================Thinking process====================");
        isFirstPrint = false;
      }
      System.out.print(reasoning);
    }
    if (content != null && !content.isEmpty()) {
      finalContent.append(content);
      if (!isFirstPrint) {
        System.out.println("\n====================Full response====================");
        isFirstPrint = true;
      }
      System.out.print(content);
    }
  }
  private static GenerationParam buildGenerationParam(Message userMsg) {
    return GenerationParam.builder()
      .apiKey(System.getenv("DASHSCOPE_API_KEY"))
      .model("deepseek-v4-pro-0813")
      .enableThinking(true)
      .incrementalOutput(true)
      .resultFormat("message")
      .messages(Arrays.asList(userMsg))
      .build();
  }
  public static void streamCallWithMessage(Generation gen, Message userMsg)
    throws NoApiKeyException, ApiException, InputRequiredException {
    GenerationParam param = buildGenerationParam(userMsg);
    Flowable<GenerationResult> result = gen.streamCall(param);
    result.blockingForEach(message -> handleGenerationResult(message));
  }
  public static void main(String[] args) {
    try {
      Generation gen = new Generation();
      Message userMsg = Message.builder().role(Role.USER.getValue()).content("Who are you?").build();
      streamCallWithMessage(gen, userMsg);
    } catch (ApiException | NoApiKeyException | InputRequiredException e) {
      System.err.println("An exception occurred: " + e.getMessage());
    }
  }
}

Example code

curl -X POST "https://maas.qwencloudapi.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "deepseek-v4-pro-0813",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Reasoning effort

deepseek-v4.1-flash, deepseek-v4-pro-0813, deepseek-v4-pro, deepseek-v4-flash, and deepseek-v4-flash-0731 have thinking mode enabled by default. You can use the reasoning_effort parameter to control reasoning intensity. Valid values: low (supported only by deepseek-v4.1-flash, deepseek-v4-flash-0731, and deepseek-v4-pro-0813), high, and max. The default value is high.

For deepseek-v4-pro and deepseek-v4-flash, low and medium produce the same behavior as high, and xhigh produces the same behavior as max. For deepseek-v4.1-flash, minimal is mapped to low, medium and xhigh are mapped to high, and ultra is mapped to max.

OpenAI compatible
DashScope

Python
Node.js
curl

from openai import OpenAI
import os

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
  model="deepseek-v4-pro-0813",
  messages=[{"role": "user", "content": "Which is larger, 9.9 or 9.11?"}],
  reasoning_effort="high",
)
print(completion.choices[0].message.content)

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: "https://maas.qwencloudapi.com/compatible-mode/v1",
});

const completion = await openai.chat.completions.create({
  model: "deepseek-v4-pro-0813",
  messages: [{ role: "user", content: "Which is larger, 9.9 or 9.11?" }],
  reasoning_effort: "high",
});
console.log(completion.choices[0].message.content);

curl -X POST https://maas.qwencloudapi.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "deepseek-v4-pro-0813",
    "messages": [{"role": "user", "content": "Which is larger, 9.9 or 9.11?"}],
    "reasoning_effort": "high"
}'

import os
from dashscope import Generation

response = Generation.call(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  model="deepseek-v4-pro-0813",
  messages=[{"role": "user", "content": "Which is larger, 9.9 or 9.11?"}],
  reasoning_effort="high",
  result_format="message",
)
print(response.output.choices[0].message.content)

Responses API

deepseek-v4.1-flash, deepseek-v4-pro-0813, deepseek-v4-flash, deepseek-v4-flash-0731, and deepseek-v4-pro support calls through the OpenAI-compatible Responses API. When calling the Responses API, you can add the web_search (Web search), web_extractor (Web extractor), and code_interpreter (Code Interpreter) tools to the tools parameter.

Python
Node.js
curl

from openai import OpenAI
import os

client = OpenAI(
  # If the environment variable is not configured, replace the following line with your API key: api_key="sk-xxx"
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)
response = client.responses.create(
  model="deepseek-v4-flash",
  input="Hello! Please introduce yourself in one sentence.",
  # Optional: enable the web search, web extractor, and code interpreter tools
  tools=[
    {"type": "web_search"},
    {"type": "web_extractor"},
    {"type": "code_interpreter"},
  ],
)
# Get the model response
print(response.output_text)

import OpenAI from "openai";

const openai = new OpenAI({
  // If the environment variable is not configured, replace the following line with your API key: apiKey: "sk-xxx"
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: "https://maas.qwencloudapi.com/compatible-mode/v1"
});
const response = await openai.responses.create({
  model: "deepseek-v4-flash",
  input: "Hello! Please introduce yourself in one sentence.",
  // Optional: enable the web search, web extractor, and code interpreter tools
  tools: [
    { type: "web_search" },
    { type: "web_extractor" },
    { type: "code_interpreter" },
  ],
});
// Get the model response
console.log(response.output_text);

curl -X POST https://maas.qwencloudapi.com/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "deepseek-v4-flash",
    "input": "Hello! Please introduce yourself in one sentence.",
    "tools": [
        {"type": "web_search"},
        {"type": "web_extractor"},
        {"type": "code_interpreter"}
    ]
}'

Multimodal examples

deepseek-v4.1-flash has native visual understanding and accepts both text and image input, returning text. All other DeepSeek models accept text input only. Through the OpenAI-compatible API, pass images as image_url content blocks. Through DashScope, use the multimodal-generation endpoint with MultiModalConversation and pass images in the image field.

OpenAI compatible
DashScope

Python
Node.js
curl

from openai import OpenAI
import os

client = OpenAI(
  # If the environment variable is not configured, replace with your API key: api_key="sk-xxx"
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)

# Single image
completion = client.chat.completions.create(
  model="deepseek-v4.1-flash",
  messages=[
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "What is in this image?"},
        {
          "type": "image_url",
          "image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}
        }
      ]
    }
  ],
)
print(completion.choices[0].message.content)

# Multiple images (uncomment to use)
# completion = client.chat.completions.create(
#     model="deepseek-v4.1-flash",
#     messages=[
#         {
#             "role": "user",
#             "content": [
#                 {"type": "text", "text": "What is in these images?"},
#                 {
#                     "type": "image_url",
#                     "image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}
#                 },
#                 {
#                     "type": "image_url",
#                     "image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"}
#                 }
#             ]
#         }
#     ],
# )
# print(completion.choices[0].message.content)

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
  // If the environment variable is not configured, replace with your API key: apiKey: "sk-xxx"
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: "https://maas.qwencloudapi.com/compatible-mode/v1"
});

const completion = await openai.chat.completions.create({
  model: "deepseek-v4.1-flash",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What is in this image?" },
        {
          type: "image_url",
          image_url: { url: "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg" }
        }
      ]
    }
  ]
});
console.log(completion.choices[0].message.content);

curl -X POST https://maas.qwencloudapi.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "deepseek-v4.1-flash",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {"type": "image_url", "image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}}
            ]
        }
    ]
}'

Python
curl

import os
from dashscope import MultiModalConversation

messages = [
  {
    "role": "user",
    "content": [
      {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
      {"text": "What is in this image?"}
    ]
  }
]

response = MultiModalConversation.call(
  # If the environment variable is not configured, replace with your API key: api_key="sk-xxx"
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  model="deepseek-v4.1-flash",
  messages=messages,
)
print(response.output.choices[0].message.content[0]["text"])

curl -X POST "https://maas.qwencloudapi.com/api/v1/services/aigc/multimodal-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "deepseek-v4.1-flash",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": [
                    {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
                    {"text": "What is in this image?"}
                ]
            }
        ]
    }
}'

Other features

Model	Multi-turn	Function calling	Web search	Context cache	Structured output
deepseek-v4.1-flash	✓	✓	✓	Implicit only	✓
deepseek-v4-pro-0813	✓	✓	✓	Implicit only	✓
deepseek-v4-pro	✓	✓	✓	✓	—
deepseek-v4-flash	✓	✓	✓	Implicit only	—
deepseek-v4-flash-0731	✓	✓	✓	Implicit only	—
deepseek-v3.2	✓	✓	✓	✓	—

Parameter defaults

Model	temperature	top_p	repetition_penalty	presence_penalty	max_tokens	thinking_budget
deepseek-v4.1-flash	1.0	0.95	-	-	393,216 shared	393,216 shared
deepseek-v4-pro-0813	1.0	0.95	-	-	393,216 shared	393,216 shared
deepseek-v4-pro	1.0	0.95	-	-	393,216 shared	393,216 shared
deepseek-v4-flash	1.0	0.95	-	-	393,216 shared	393,216 shared
deepseek-v4-flash-0731	1.0	0.95	-	-	393,216 shared	393,216 shared
deepseek-v3.2	1.0	0.95	-	-	65,536	32,768

A hyphen (-) indicates that the parameter is not supported.
The deepseek-r1, deepseek-r1-0528, and distilled models do not support overriding their default parameter values.
For parameter descriptions, see the OpenAI-compatible Chat API.

​Quick start

​Reasoning effort

​Responses API

​Multimodal examples

​Other features

​Parameter defaults

Quick start

Reasoning effort

Responses API

Multimodal examples

Other features

Parameter defaults