DeepSeek: DeepSeek V3.2 Exp

DeepSeek V3.2 Exp is an experimental large language model from DeepSeek, released on 29 September 2025 as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism. The model is text only in both directions, and its knowledge cutoff is 31 July 2025.

It accepts text input and returns text output, with a context window of 163,840 tokens and a maximum output of 147,456 tokens. That combination suits long documents and long generations in the same request. The supported parameters include temperature, top_p, top_k, frequency_penalty, max_tokens and response_format, along with structured_outputs for schema-constrained replies. It also exposes reasoning controls through the reasoning and include_reasoning parameters, and tool use through tools and tool_choice, so it can call functions and return structured arguments rather than prose alone.

Those capabilities point to a few practical patterns. A large context window and a large output budget make it a reasonable fit for summarising or extracting from long files, multi-document question answering, and drafting long-form text in one pass. Structured outputs and tool calling suit agents, retrieval pipelines and workflows where the model must return JSON that matches a schema or decide which function to invoke. The reasoning parameters let you request or suppress reasoning content depending on whether you want the intermediate steps surfaced. Because it is experimental, it is worth testing against your own evaluation set before depending on it in production.

You call it through api14's OpenAI-compatible API using the model id "deepseek/deepseek-v3.2-exp". Point your existing OpenAI client at the api14 base URL, send a chat completions request with that model id, and the request body accepts the parameters listed above: frequency_penalty, include_reasoning, max_tokens, reasoning, response_format, structured_outputs, temperature, tool_choice, tools, top_k and top_p. If you already use an OpenAI SDK, switching is mostly a change of base URL, API key and model id.

The main caveat is the experimental status. It is a text model, so it does not handle images or audio, and if your task is multimodal you would need a different model. If you need a stable, non-experimental endpoint for long-lived production traffic, a settled release may be the safer default, with this model used where its sparse attention and reasoning controls are worth evaluating.