Mistral AI Provider

The Mistral AI provider contains language model support for the Mistral chat API.

Setup

The Mistral provider is available in the @ai-sdk/mistral module. You can install it with

pnpm add @ai-sdk/mistral

Provider Instance

You can import the default provider instance mistral from @ai-sdk/mistral:

import { mistral } from '@ai-sdk/mistral';

If you need a customized setup, you can import createMistral from @ai-sdk/mistral and create a provider instance with your settings:

import { createMistral } from '@ai-sdk/mistral';

const mistral = createMistral({
  // custom settings
});

You can use the following optional settings to customize the Mistral provider instance:

• baseURL string

  Use a different URL prefix for API calls, e.g. to use proxy servers. The default prefix is https://api.mistral.ai/v1.

• apiKey string

  API key that is being sent using the Authorization header. It defaults to the MISTRAL_API_KEY environment variable.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

  Custom fetch implementation. Defaults to the global fetch function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.

Language Models

You can create models that call the Mistral chat API using a provider instance. The first argument is the model id, e.g. mistral-large-latest. Some Mistral chat models support tool calls.

const model = mistral('mistral-large-latest');

Mistral chat models also support additional model settings that are not part of the standard call settings. You can pass them as an options argument and utilize MistralLanguageModelOptions for typing:

import { mistral, type MistralLanguageModelOptions } from '@ai-sdk/mistral';
const model = mistral('mistral-large-latest');

await generateText({
  model,
  providerOptions: {
    mistral: {
      safePrompt: true, // optional safety prompt injection
    } satisfies MistralLanguageModelOptions,
  },
});

The following optional provider options are available for Mistral models:

• safePrompt boolean

  Whether to inject a safety prompt before all conversations.

  Defaults to false.

• documentImageLimit number

  Maximum number of images to process in a document.

• documentPageLimit number

  Maximum number of pages to process in a document.

• strictJsonSchema boolean

  Whether to use strict JSON schema validation for structured outputs. Only applies when a schema is provided and only sets the strict flag in addition to using Custom Structured Outputs, which is used by default if a schema is provided.

  Defaults to false.

• structuredOutputs boolean

  Whether to use structured outputs. When enabled, tool calls and object generation will be strict and follow the provided schema.

  Defaults to true.

Document OCR

Mistral chat models support document OCR for PDF files. You can optionally set image and page limits using the provider options.

const result = await generateText({
  model: mistral('mistral-small-latest'),
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'What is an embedding model according to this document?',
        },
        {
          type: 'file',
          data: new URL(
            'https://github.com/vercel/ai/blob/main/examples/ai-core/data/ai.pdf?raw=true',
          ),
          mediaType: 'application/pdf',
        },
      ],
    },
  ],
  // optional settings:
  providerOptions: {
    mistral: {
      documentImageLimit: 8,
      documentPageLimit: 64,
    },
  },
});

Reasoning Models

Mistral offers reasoning models that provide step-by-step thinking capabilities:

• magistral-small-2506: Smaller reasoning model for efficient step-by-step thinking
• magistral-medium-2506: More powerful reasoning model balancing performance and cost

These models return content that includes <think>...</think> tags containing the reasoning process. To properly extract and separate the reasoning from the final answer, use the extract reasoning middleware:

import { mistral } from '@ai-sdk/mistral';
import {
  extractReasoningMiddleware,
  generateText,
  wrapLanguageModel,
} from 'ai';

const result = await generateText({
  model: wrapLanguageModel({
    model: mistral('magistral-small-2506'),
    middleware: extractReasoningMiddleware({
      tagName: 'think',
    }),
  }),
  prompt: 'What is 15 * 24?',
});

console.log('REASONING:', result.reasoningText);
// Output: "Let me calculate this step by step..."

console.log('ANSWER:', result.text);
// Output: "360"

The middleware automatically parses the <think> tags and provides separate reasoningText and text properties in the result.

Example

You can use Mistral language models to generate text with the generateText function:

import { mistral } from '@ai-sdk/mistral';
import { generateText } from 'ai';

const { text } = await generateText({
  model: mistral('mistral-large-latest'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

Mistral language models can also be used in the streamText, generateObject, and streamObject functions (see AI SDK Core).

Structured Outputs

Mistral chat models support structured outputs using JSON Schema. You can use generateObject or streamObject with Zod, Valibot, or raw JSON Schema. The SDK sends your schema via Mistral's response_format: { type: 'json_schema' }.

import { mistral } from '@ai-sdk/mistral';
import { generateObject } from 'ai';
import { z } from 'zod';

const result = await generateObject({
  model: mistral('mistral-large-latest'),
  schema: z.object({
    recipe: z.object({
      name: z.string(),
      ingredients: z.array(z.string()),
      instructions: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a simple pasta recipe.',
});

console.log(JSON.stringify(result.object, null, 2));

You can enable strict JSON Schema validation using a provider option:

import { mistral } from '@ai-sdk/mistral';
import { generateObject } from 'ai';
import { z } from 'zod';

const result = await generateObject({
  model: mistral('mistral-large-latest'),
  providerOptions: {
    mistral: {
      strictJsonSchema: true, // reject outputs that don't strictly match the schema
    },
  },
  schema: z.object({
    title: z.string(),
    items: z.array(z.object({ id: z.string(), qty: z.number().int().min(1) })),
  }),
  prompt: 'Generate a small shopping list.',
});

Model Capabilities

Embedding Models

You can create models that call the Mistral embeddings API using the .textEmbedding() factory method.

const model = mistral.textEmbedding('mistral-embed');

You can use Mistral embedding models to generate embeddings with the embed function:

import { mistral } from '@ai-sdk/mistral';
import { embed } from 'ai';

const { embedding } = await embed({
  model: mistral.textEmbedding('mistral-embed'),
  value: 'sunny day at the beach',
});

Model Capabilities