Mistral: Devstral 2 2512

Mistral: Devstral 2 2512 is an open-source model from Mistral AI, released on 9 December 2025. It is a 123B-parameter dense transformer built specifically for agentic coding, meaning it is designed to work inside tool-driven workflows rather than only answering single-shot prompts. It sits in the text category, and the model id on api14 is "mistralai/devstral-2512".

The model accepts text and file inputs and returns text. Its context window is 262144 tokens, with a maximum output of 209715 tokens, so it can hold large codebases, long logs or multi-file diffs in a single request and still produce substantial output. Supported request parameters are frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools and top_p. Tool calling and structured outputs are part of that set, which matters for agentic coding: the model can be given function definitions, choose among them, and return responses in a schema you define.

Those capabilities point to a fairly specific set of uses. Agentic coding assistants that read files, run commands and iterate on a repository are the clearest fit, since the model was built for that pattern and the file input modality lets you pass source material directly. Code review and refactoring across large files also follow from the long context and large output allowance. Because tools and structured outputs are supported, you can wire it into pipelines that need deterministic JSON, such as extracting structured data from documents or driving multi-step automation where each step returns a typed result. The seed parameter helps when you want repeatable runs during testing.

Calling it through api14 works like any other OpenAI-compatible model. Point your client at the api14 base URL, send a chat completions request, and set the model field to "mistralai/devstral-2512". The parameters listed above can be passed in the request body in the usual OpenAI format, including tools and tool_choice for function calling and response_format or structured_outputs when you need constrained output. No separate SDK is required if you already have an OpenAI-compatible client.

One practical limit to keep in mind is modality: input is text and file, output is text only, so image or audio work would need a different model. The context window is large but not unlimited, and very long agent sessions still need trimming or summarisation. If your task is general conversation rather than coding or tool use, a lighter general-purpose model may be a better fit.