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Reasoning Model (deepseek-reasoner) 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 these parameters will not trigger an error but will also have no effect. 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 ) # ... Previous Get User Balance Next Multi-round Conversation API Parameters Multi-round Conversation API Example