Mistral: Mistral Medium 3.1

Mistral Medium 3.1 is an enterprise-grade text model from Mistral, an updated version of Mistral Medium 3. Mistral positions it as a high-performance model that delivers frontier-level capabilities, and api14 exposes it under the model id mistralai/mistral-medium-3.1. It sits in the text category, so it is built for language work rather than audio or video, and the provider release date is 13 August 2025.

On the input side the model is multimodal: it accepts text, images and files, and it returns text. That combination matters for developers who need to feed a document, a screenshot or a scanned page into the same request as their instructions. The context window is 131,072 tokens, and the maximum output is 104,857 tokens, which is large enough for long generated documents rather than short replies only. The knowledge cutoff is 30 June 2025, so anything after that date has to come from the prompt or from a tool call. Supported request parameters include temperature, top_p, frequency_penalty, presence_penalty, max_tokens, stop, seed, response_format, structured_outputs, tools and tool_choice.

Those capabilities point to a few practical patterns. The file and image inputs suit document question answering, contract or report review, and extraction from PDFs or slides where the answer has to be grounded in the supplied material. The long context supports summarising or comparing several long documents in one call. Tool calling and structured outputs make it a reasonable fit for agent workflows and for pipelines where the model must return JSON that another service parses. The seed and penalty parameters give you some control over reproducibility and repetition when you are tuning prompts.

Through api14 you call it on the OpenAI-compatible chat completions endpoint, passing the model id mistralai/mistral-medium-3.1 with your messages. Because the surface is OpenAI-compatible, existing SDKs and clients work with a base URL change, and you can send the parameters listed above: temperature, top_p, frequency_penalty, presence_penalty, max_tokens, stop, seed, response_format, structured_outputs, tools and tool_choice. That means tool definitions and structured output schemas can be declared in the request rather than parsed out of free text afterwards.

The main constraint to keep in mind is scope. Output is text only, so it is not the model for image or audio generation, and its knowledge is frozen at 30 June 2025, which means recent facts belong in the prompt or should be retrieved through tools. If your workload needs a modality this model does not emit, or if you need a smaller and faster option for simple classification, another model on api14 may fit better. Otherwise it is a straightforward choice for text and document tasks that benefit from a large context window, image and file inputs, and tool calling.