Mistral: Mixtral 8x22B Instruct

Mistral: Mixtral 8x22B Instruct is a text model from Mistral, released by the provider on 17 April 2024. It is the official instruct fine-tuned version of Mixtral 8x22B, a sparse mixture-of-experts design that uses 39B active parameters out of 141B. The instruct tuning adapts the base model for following directions and answering requests directly, which is what most application developers want when they call a model through an API.

The model takes text and file inputs and returns text. Its context window is 65,536 tokens, and the maximum output is 52,428 tokens, so long inputs and long generations are both possible within the same request. The knowledge cutoff is 31 January 2024, which means anything after that date is outside what the model was trained on. Mistral highlights strong math and coding as particular strengths of this model, alongside cost efficiency for its size.

Those capabilities point to a fairly wide set of practical uses. The long context suits working over large documents, whether that means summarizing a long report, extracting structured details from a file, or answering questions against a body of text supplied in the prompt. The math and coding strengths make it a reasonable fit for code assistance, such as explaining or generating code, and for quantitative tasks that need step-by-step reasoning. Because it accepts files as input, it can be used in pipelines where documents are passed in directly rather than pasted as plain text. Tool calling and structured outputs also make it suitable for agent-style workflows and for producing responses that downstream code can parse.

You call it through api14's OpenAI-compatible API using the model id "mistralai/mixtral-8x22b-instruct". The supported request parameters are frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools and top_p. In practice that covers the usual sampling controls (temperature, top_p, penalties, seed, stop), output length via max_tokens, response shaping via response_format and structured_outputs, and function calling via tools and tool_choice. If you already have code written against the OpenAI chat completions shape, switching to this model is mostly a matter of changing the model id.

One thing to keep in mind is the knowledge cutoff of 31 January 2024, so questions about later events will not be answered from training data and may need retrieval or prompt-supplied context. The model is text-only on the output side, so tasks that require generating images or audio are out of scope. If your workload depends on very recent information or on modalities beyond text and file input, another model may fit better.