How is the Mistral API authenticated and shaped?

You send a POST to the chat completions endpoint with an Authorization Bearer header carrying your API key, a model field, and a messages array of role/content objects. The request and response shape closely mirror the OpenAI chat format, so most OpenAI client code ports to Mistral by swapping the base URL, key, and model name.

Which Mistral model should I pick?

Match the tier to the task. Small models are cheap and fast for classification, extraction, and high-volume simple chat. Medium balances cost and capability for general assistants. Large handles the hardest reasoning, code, and multi-step tasks. Start small, measure quality on your real prompts, and only move up a tier when the smaller model misses.

Does Mistral support function calling?

Yes. Pass a tools array describing your functions with JSON-schema parameters; the model decides when to call one and returns the function name with arguments you then execute, feeding the result back into the conversation. It works like OpenAI tool calling and is how you connect Mistral to databases, search, and external APIs.

How do I force valid JSON output?

Set response_format to type json_object and instruct the model in the prompt to return JSON matching your shape. This guarantees parseable JSON so you avoid brittle regex extraction. For strict schemas, validate the parsed object on your side and retry on mismatch — JSON mode guarantees valid JSON, not that it matches your exact schema.

What makes Mistral attractive versus closed models?

Mistral models are competitive on quality while being notably cheaper per token, and several are openly licensed so you can self-host for data residency or cost control. That combination — affordable API plus an open-weight escape hatch — makes Mistral a strong default for high-volume workloads where flagship pricing would dominate the budget.

Getting Started with the Mistral AI API

What you are building

This guide gets you from zero to a working Mistral API integration. Mistral’s appeal is a blend of competitive quality, low per-token cost, and openly licensed models you can self-host — a strong default when flagship pricing would dominate your budget. The API mirrors the familiar OpenAI chat format, so if you have called any chat model before, the shape will feel immediately familiar; you mostly swap the base URL, key, and model name.

How the API works

You authenticate with an Authorization: Bearer <key> header and POST to the chat completions endpoint with three things: a model name, a messages array of { role, content } objects (system, user, assistant), and optional parameters like temperature and max_tokens. The model returns a completion you read from choices[0].message.content. From there, two features unlock production use. JSON mode — response_format: { type: "json_object" } — guarantees the output parses as valid JSON so you skip brittle text scraping. Function calling — a tools array of JSON-schema function descriptions — lets the model decide to call your code, returning a structured function name and arguments you execute and feed back. Streaming works too: set stream: true and read tokens as they arrive for a responsive UI.

Picking a tier and the planner below

The biggest lever is model choice, because tiers differ several-fold in price. Use a small model for classification, extraction, and high-volume simple chat; medium for balanced general assistants; large only for the hardest reasoning, code, and multi-step work. The discipline that saves the most money is to start small, measure quality on your real prompts, and step up only when the smaller model demonstrably misses. The planner below lets you compare cost across the tiers for your own token volume so you can see exactly what each rung of the ladder costs before you commit.