📝 简介

Deepseek-reasoner 是 DeepSeek 推出的推理模型。在输出最终回答之前，模型会先输出一段思维链内容，以提升最终答案的准确性。API 向用户开放 deepseek-reasoner 思维链的内容，以供用户查看、展示、蒸馏使用。

💡 请求示例

基础文本对话 ✅

curl https://api.deepseek.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $NEWAPI_API_KEY" \
  -d '{
    "model": "deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": "9.11 and 9.8, which is greater?"
      }
    ],
    "max_tokens": 4096
  }'

响应示例:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "deepseek-reasoner",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "reasoning_content": "让我一步步思考:\n1. 我们需要比较9.11和9.8的大小\n2. 两个数都是小数,我们可以直接比较\n3. 9.8 = 9.80\n4. 9.11 < 9.80\n5. 所以9.8更大",
      "content": "9.8 is greater than 9.11."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 15,
    "total_tokens": 25
  }
}

流式响应 ✅

curl https://api.deepseek.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $NEWAPI_API_KEY" \
  -d '{
    "model": "deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": "9.11 and 9.8, which is greater?"
      }
    ],
    "stream": true
  }'

流式响应示例:

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"让我"},"finish_reason":null}]}

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{"reasoning_content":"一步步"},"finish_reason":null}]}

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{"reasoning_content":"思考:"},"finish_reason":null}]}

// ... 更多思维链内容 ...

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{"content":"9.8"},"finish_reason":null}]}

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{"content":" is greater"},"finish_reason":null}]}

// ... 更多最终答案内容 ...

{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"deepseek-reasoner","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

📮 请求

端点

POST /v1/chat/completions

鉴权方法

在请求头中包含以下内容进行 API 密钥认证：

Authorization: Bearer $NEWAPI_API_KEY

其中 $DEEPSEEK_API_KEY 是您的 API 密钥。

请求体参数

`messages`

类型：数组
必需：是

到目前为止包含对话的消息列表。请注意,如果您在输入的 messages 序列中传入了 reasoning_content，API 会返回 400 错误。

`model`

类型：字符串
必需：是
值：deepseek-reasoner

要使用的模型 ID。目前仅支持 deepseek-reasoner。

`max_tokens`

类型：整数
必需：否
默认值：4096
最大值：8192

最终回答的最大长度（不含思维链输出）。请注意，思维链的输出最多可以达到 32K tokens。

`stream`

类型：布尔值
必需：否
默认值：false

是否使用流式响应。

不支持的参数

以下参数当前不支持:

temperature
top_p
presence_penalty
frequency_penalty
logprobs
top_logprobs

注意:为了兼容已有软件,设置 temperature、top_p、presence_penalty、frequency_penalty 参数不会报错,但也不会生效。设置 logprobs、top_logprobs 会报错。

支持的功能

对话补全
对话前缀续写 (Beta)

不支持的功能

Function Call
Json Output
FIM 补全 (Beta)

📥 响应

成功响应

返回一个聊天补全对象，如果请求被流式传输，则返回聊天补全块对象的流式序列。

`id`

类型：字符串
说明：响应的唯一标识符

`object`

类型：字符串
说明：对象类型,值为 "chat.completion"

`created`

类型：整数
说明：响应创建时间戳

`model`

类型：字符串
说明：使用的模型名称,值为 "deepseek-reasoner"

`choices`

类型：数组
说明：包含生成的回复选项
属性:
index: 选项索引
message: 包含角色、思维链内容和最终回答的消息对象
- role: 角色,值为 "assistant"
- reasoning_content: 思维链内容
- content: 最终回答内容
finish_reason: 完成原因

`usage`

类型：对象
说明：token 使用统计
属性:
prompt_tokens: 提示使用的 token 数
completion_tokens: 补全使用的 token 数
total_tokens: 总 token 数

📝 上下文拼接说明

在每一轮对话过程中，模型会输出思维链内容（reasoning_content）和最终回答（content）。在下一轮对话中，之前轮输出的思维链内容不会被拼接到上下文中，如下图所示：

Deepseek reasoning 上下文拼接示意图

注意

如果您在输入的 messages 序列中，传入了reasoning_content，API 会返回 400 错误。因此，请删除 API 响应中的 reasoning_content 字段，再发起 API 请求，方法如下方使用示例所示。

使用示例:

from openai import OpenAI
client = OpenAI(api_key="<DeepSeek API Key>", base_url="https://api.deepseek.com")

# 第一轮对话
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages
)

reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content

# 第二轮对话 - 只拼接最终回答content
messages.append({'role': 'assistant', 'content': content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
    model="deepseek-reasoner", 
    messages=messages
)

#__codelineno-6-1

流式响应示例:

# 第一轮对话
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages,
    stream=True
)

reasoning_content = ""
content = ""

for chunk in response:
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content
    else:
        content += chunk.choices[0].delta.content

# 第二轮对话 - 只拼接最终回答content
messages.append({"role": "assistant", "content": content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages,
    stream=True
)