📝 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：
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 OpenAIclient = 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_contentcontent = response.choices[0].message.content# 第二轮对话 - 只拼接最终回答contentmessages.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)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contentmessages.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)
