Mistral: Mistral Large 3 2512 (batch)

Mistral Large 3 2512 is the instruction-tuned Mistral AI edition for general conversation, visual understanding and enterprise work. Its sparse mixture-of-experts architecture has 675 billion total parameters, with 41 billion active, and a 256k context window. Text and images can contribute to the same task, while the answer is text, code or a structured result.

Capabilities and input preparation

The model combines a language component with a vision encoder. Give an image a clear role: a page to read, a diagram to interpret or a screen to explain. For a crowded screen, provide a crop containing the relevant controls and retain the surrounding labels needed to understand them. Mistral recommends image shapes close to square; break an exceptionally narrow or wide source into useful regions instead of shrinking important details into an unreadable strip.

The maker lists multilingual conversation across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. State the output language, intended reader and terminology to preserve. For a support article, ask for a localized explanation with product names unchanged and a short list of uncertain translations to resolve before publication.

Long documents and enterprise knowledge

Use the context to bring related material together: policy text, a specification, existing decisions and the question that needs an answer. Label each source and its revision. A useful request is: “Compare these three onboarding policies. Return a table of shared requirements, conflicts and unanswered questions, with the source section for each finding.” The resulting artifact is a comparison grounded in the supplied passages rather than a generic summary of the topic.

For a document question, identify the desired level of detail and request the supporting passage next to the conclusion. Mistral's Document QnA workflow first extracts text and document structure with OCR, then uses a language model to interpret that material. Preserve page numbers and headings when preparing extracted text so the answer can point back to the original. A document URL, extracted text and image evidence have different preparation requirements; choose the input form supported by the application you are using.

Coding and everyday assistance

Mistral positions Large 3 for coding assistance as well as daily conversation. Supply the relevant functions, expected behavior and observed failure. For example: “This parser drops the last field when the input ends without a newline. Explain the failing branch, propose the smallest change and return the corrected function with three representative input/output examples.” The useful result is a proposed change tied to the actual code and a set of cases the developer can run.

For a business draft, provide the facts and desired artifact together. A request to turn meeting notes into a decision memo should name the decision, alternatives, unresolved assumptions and next action. Ask the model to keep source facts separate from recommendations and to mark missing information within the appropriate section. This makes the output easier to revise than an undifferentiated narrative.

Tools and structured results

Native function calling lets the model choose a supplied function and prepare its arguments. The application runs that function and returns its result; the model then uses the returned information in the answer. Keep tools specific and expose only those needed for the task. A stock-check assistant can receive a product reference, request a stock lookup and explain the returned quantity and warehouse. Validate the selected function and arguments before executing the request, and retain the tool response alongside the final answer.

For extraction, specify the fields, types and treatment of missing values. Mistral documents JSON mode and custom structured output; its JSON mode requires an explicit instruction to return JSON, while a custom schema supplies stronger format control. An invoice summary can ask for supplier, invoice number, due date, currency and line items, with missing values represented consistently. Inspect the extracted values against the document, even when the response matches the requested structure.

Settings and edition differences

Define the role, task and tool rules in the system instructions. Mistral recommends a temperature below 0.1 for production and everyday use of this checkpoint, with higher values explored for creative tasks. Separate a request for varied ideas from a request for a reproducible extraction: give the former room for alternatives and the latter explicit fields and source constraints.

This instruction edition is separate from the Large 3 base checkpoint and from dedicated reasoning models. The maker identifies strict reasoning and specialized visual work as areas where a model designed specifically for those tasks can be preferable. For a difficult calculation, supply the assumptions and units, ask for intermediate checks and use an available calculation tool to verify the result. For dense visual evidence, prepare readable regions and compare the answer with the source before using it in a report.