Yes. DeepSeek-V3.2 provides server-side reasoning through the deepseek-reasoner endpoint, which automatically generates chain-of-thought (CoT) reasoning on the server and returns it in a separate field called reasoning_content. Unlike prompting techniques that attempt to elicit reasoning manually, this reasoning mode is designed and managed by the model provider, making it more stable and predictable. This also means you do not need to craft special prompts to “unlock” step-by-step thinking—the model always uses it internally.
There are important constraints. First, deepseek-reasoner does not support function calling; if you send tools to this endpoint, the API silently redirects execution to deepseek-chat. Second, reasoning mode does not use sampling parameters such as temperature or top_p; the model behaves closer to a solver, where the main control is max_tokens. Third, the API forbids feeding previous reasoning_content back into the context; if you attempt this, the model will return an error. This design avoids the model “learning from its own thoughts,” which can degrade performance.
In practice, many developers treat deepseek-reasoner as an analysis module in their pipeline. For example, a RAG system might pass retrieved chunks from a vector database such as Milvus or Zilliz Cloud into a call to deepseek-reasoner. The model returns a detailed chain-of-thought for debugging and a clean final answer for user consumption. This separation lets teams inspect failures—whether they came from retrieval, analysis, or reasoning—without exposing chain-of-thought to end users if policy prohibits it.