API Guides

deepseek-reasoner is a reasoning model developed by DeepSeek. Before delivering the final answer, the model first generates a Chain of Thought (CoT) to enhance the accuracy of its responses. Our API provides users with access to the CoT content generated by deepseek-reasoner, enabling them to view, display, and distill it.

When using deepseek-reasoner, please upgrade the OpenAI SDK first to support the new parameters.

pip3 install -U openai

API Parameters​

• Input：

  • max_tokens：The maximum length of the final response after the CoT output is completed, defaulting to 4K, with a maximum of 8K. Note that the CoT output can reach up to 32K tokens, and the parameter to control the CoT length (reasoning_effort) will be available soon.

• Output：

  • reasoning_content：The content of the CoT，which is at the same level as content in the output structure. See API Example for details

  • contentThe content of the final answer

• Context Length：The API supports a maximum context length of 64K, and the length of the output reasoning_content is not counted within the 64K context length.

• Supported Features：Chat Completion、Chat Prefix Completion (Beta)

• Not Supported Features：Function Call、Json Output、FIM (Beta)

• Not Supported Parameters：temperature、top_p、presence_penalty、frequency_penalty、logprobs、top_logprobs. Please note that to ensure compatibility with existing software, setting temperature、top_p、presence_penalty、frequency_penalty will not trigger an error but will also have no effect. Setting logprobs、top_logprobs will trigger an error.

Multi-round Conversation​

In each round of the conversation, the model outputs the CoT (reasoning_content) and the final answer (content). In the next round of the conversation, the CoT from previous rounds is not concatenated into the context, as illustrated in the following diagram:

Please note that if the reasoning_content field is included in the sequence of input messages, the API will return a 400 error. Therefore, you should remove the reasoning_content field from the API response before making the API request, as demonstrated in the API example.

API Example​

The following code, using Python as an example, demonstrates how to access the CoT and the final answer, as well as how to conduct multi-round conversations:

• NoStreaming

• Streaming

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

# Round 1
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

# Round 2
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
)
# ...