Mistral Large 2407

Mistral Large 2407 is a text generation model from Mistral, identified on api14 as mistralai/mistral-large-2407. It is Mistral's flagship model, Mistral Large 2, in its 2407 version, and Mistral describes it as a proprietary weights-available model. It was released by the provider on 2024-11-19 and has a knowledge cutoff of 2024-03-31.

The model accepts text and file inputs and returns text. It has a context window of 131072 tokens and a maximum output of 104857 tokens, so long inputs and long generations are both possible within the same request. Mistral positions it as strong at reasoning, code, JSON and chat. The supported request parameters include temperature, top_p, frequency_penalty, presence_penalty, max_tokens, stop, seed, response_format, structured_outputs, tools and tool_choice.

Because it handles reasoning, code and JSON well, it suits tasks such as multi-step analysis, code generation and review, and chat assistants that need to hold a long conversation or a large document in context. The file input modality makes it usable for workflows where a document is supplied alongside a question, and the structured output and tool calling parameters make it a reasonable fit for pipelines that need machine-readable responses or that connect the model to external functions. The large output ceiling helps when the answer itself is long, for example a full file, a detailed report or an extended explanation.

You call it through api14's OpenAI-compatible API using the model id "mistralai/mistral-large-2407". Requests follow the OpenAI chat completions shape, so you send messages and set parameters in the usual fields. The model supports frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools and top_p. Use response_format or structured_outputs when you need JSON, and tools with tool_choice when you want the model to call functions you define. The seed parameter lets you make sampling more reproducible, and stop lets you cut generation at a chosen sequence.

Keep the knowledge cutoff of 2024-03-31 in mind: the model will not know about events or library changes after that date, so for recent information you should supply it in the prompt or through a file. If your task is not text based, this model will not fit, since it only takes text and file inputs and only produces text. Within those bounds it is a general choice for reasoning, code and JSON work, and the parameter list gives you enough control over sampling, output shape and tool use to integrate it into existing OpenAI-compatible code with little change.