promptfoo/site/docs/providers/mistral.md

sidebar_label	title	description	keywords
Mistral AI	Mistral AI Provider - Complete Guide to Models, Reasoning, and API Integration	Configure Mistral AI Magistral reasoning models with multimodal capabilities, function calling, and OpenAI-compatible APIs	[mistral ai magistral reasoning openai alternative llm evaluation function calling multimodal ai code generation mistral api]

Mistral AI

The Mistral AI API provides access to cutting-edge language models that deliver exceptional performance at competitive pricing. Mistral offers a compelling alternative to OpenAI and other providers, with specialized models for reasoning, code generation, and multimodal tasks.

Mistral is particularly valuable for:

• Cost-effective AI integration with pricing up to 8x lower than competitors
• Advanced reasoning with Magistral models that show step-by-step thinking
• Code generation excellence with Codestral models supporting 80+ programming languages
• Multimodal capabilities for text and image processing
• Enterprise deployments with on-premises options requiring just 4 GPUs
• Multilingual applications with native support for 12+ languages

:::tip Why Choose Mistral?

Mistral Medium 3 offers GPT-4 class performance at $0.40/$2.00 per million tokens (input/output), representing significant cost savings compared to OpenAI's $2.50/$10.00 pricing for similar capabilities.

:::

API Key

To use Mistral AI, you need to set the MISTRAL_API_KEY environment variable, or specify the apiKey in the provider configuration.

Example of setting the environment variable:

export MISTRAL_API_KEY=your_api_key_here

Configuration Options

The Mistral provider supports extensive configuration options:

Basic Options

providers:
  - id: mistral:mistral-large-latest
    config:
      # Model behavior
      temperature: 0.7 # Creativity (0.0-2.0)
      top_p: 0.95 # Nucleus sampling (0.0-1.0)
      max_tokens: 4000 # Response length limit

      # Advanced options
      safe_prompt: true # Content filtering
      random_seed: 42 # Deterministic outputs
      frequency_penalty: 0.1 # Reduce repetition
      presence_penalty: 0.1 # Encourage diversity

JSON Mode

Force structured JSON output:

providers:
  - id: mistral:mistral-large-latest
    config:
      response_format:
        type: 'json_object'
      temperature: 0.3 # Lower temp for consistent JSON

tests:
  - vars:
      prompt: "Extract name, age, and occupation from: 'John Smith, 35, engineer'. Return as JSON."
    assert:
      - type: is-json
      - type: javascript
        value: JSON.parse(output).name === "John Smith"

Authentication Configuration

providers:
  # Option 1: Environment variable (recommended)
  - id: mistral:mistral-large-latest

  # Option 2: Direct API key (not recommended for production)
  - id: mistral:mistral-large-latest
    config:
      apiKey: 'your-api-key-here'

  # Option 3: Custom environment variable
  - id: mistral:mistral-large-latest
    config:
      apiKeyEnvar: 'CUSTOM_MISTRAL_KEY'

  # Option 4: Custom endpoint
  - id: mistral:mistral-large-latest
    config:
      apiHost: 'custom-proxy.example.com'
      apiBaseUrl: 'https://custom-api.example.com/v1'

Advanced Model Configuration

providers:
  # Reasoning model with optimal settings
  - id: mistral:magistral-medium-latest
    config:
      temperature: 0.7
      top_p: 0.95
      max_tokens: 40960 # Full context for reasoning

  # Code generation with FIM support
  - id: mistral:codestral-latest
    config:
      temperature: 0.2 # Low for consistent code
      max_tokens: 8000
      stop: ['```'] # Stop at code block end

  # Multimodal configuration
  - id: mistral:pixtral-12b
    config:
      temperature: 0.5
      max_tokens: 2000
      # Image processing options handled automatically

Environment Variables Reference

Variable	Description	Example
MISTRAL_API_KEY	Your Mistral API key (required)	sk-1234...
MISTRAL_API_HOST	Custom hostname for proxy setup	api.example.com
MISTRAL_API_BASE_URL	Full base URL override	https://api.example.com/v1

Model Selection

You can specify which Mistral model to use in your configuration. The following models are available:

Chat Models

Premier Models

Model	Context	Input Price	Output Price	Best For
mistral-large-latest	128k	$2.00/1M	$6.00/1M	Complex reasoning, enterprise tasks
mistral-medium-latest	128k	$0.40/1M	$2.00/1M	Balanced performance and cost
codestral-latest	256k	$0.30/1M	$0.90/1M	Code generation, 80+ languages
magistral-medium-latest	40k	$2.00/1M	$5.00/1M	Advanced reasoning, step-by-step thinking

Free Models

Model	Context	Input Price	Output Price	Best For
mistral-small-latest	128k	$0.10/1M	$0.30/1M	General tasks, cost-effective
magistral-small-latest	40k	$0.50/1M	$1.50/1M	Reasoning on a budget
open-mistral-nemo	128k	$0.15/1M	$0.15/1M	Multilingual, research
pixtral-12b	128k	$0.15/1M	$0.15/1M	Vision + text, multimodal

Legacy Models (Deprecated)

1. open-mistral-7b, mistral-tiny, mistral-tiny-2312
2. open-mistral-nemo, open-mistral-nemo-2407, mistral-tiny-2407, mistral-tiny-latest
3. mistral-small-2402
4. mistral-medium-2312, mistral-medium
5. mistral-large-2402
6. mistral-large-2407
7. codestral-2405
8. codestral-mamba-2407, open-codestral-mamba, codestral-mamba-latest
9. open-mixtral-8x7b, mistral-small, mistral-small-2312
10. open-mixtral-8x22b, open-mixtral-8x22b-2404

Embedding Model

• mistral-embed - $0.10/1M tokens - 8k context

Here's an example config that compares different Mistral models:

providers:
  - mistral:mistral-medium-latest
  - mistral:mistral-small-latest
  - mistral:open-mistral-nemo
  - mistral:magistral-medium-latest
  - mistral:magistral-small-latest

Reasoning Models

Mistral's Magistral models are specialized reasoning models announced in June 2025. These models excel at multi-step logic, transparent reasoning, and complex problem-solving across multiple languages.

Key Features of Magistral Models

• Chain-of-thought reasoning: Models provide step-by-step reasoning traces before arriving at final answers
• Multilingual reasoning: Native reasoning capabilities across English, French, Spanish, German, Italian, Arabic, Russian, Chinese, and more
• Transparency: Traceable thought processes that can be followed and verified
• Domain expertise: Optimized for structured calculations, programmatic logic, decision trees, and rule-based systems

Magistral Model Variants

• Magistral Small (magistral-small-2506): 24B parameter open-source version under Apache 2.0 license
• Magistral Medium (magistral-medium-2506): More powerful enterprise version with enhanced reasoning capabilities

Usage Recommendations

For reasoning tasks, consider using these parameters for optimal performance:

providers:
  - id: mistral:magistral-medium-latest
    config:
      temperature: 0.7
      top_p: 0.95
      max_tokens: 40960 # Recommended for reasoning tasks

Multimodal Capabilities

Mistral offers vision-capable models that can process both text and images:

Image Understanding

Use pixtral-12b for multimodal tasks:

providers:
  - id: mistral:pixtral-12b
    config:
      temperature: 0.7
      max_tokens: 1000

tests:
  - vars:
      prompt: 'What do you see in this image?'
      image: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD...'

Supported Image Formats

• JPEG, PNG, GIF, WebP
• Maximum size: 20MB per image
• Resolution: Up to 2048x2048 pixels optimal

Function Calling & Tool Use

Mistral models support advanced function calling for building AI agents and tools:

providers:
  - id: mistral:mistral-large-latest
    config:
      temperature: 0.1
      tools:
        - type: function
          function:
            name: get_weather
            description: Get current weather for a location
            parameters:
              type: object
              properties:
                location:
                  type: string
                  description: City name
                unit:
                  type: string
                  enum: ['celsius', 'fahrenheit']
              required: ['location']

tests:
  - vars:
      prompt: "What's the weather like in Paris?"
    assert:
      - type: contains
        value: 'get_weather'

Tool Calling Best Practices

• Use low temperature (0.1-0.3) for consistent tool calls
• Provide detailed function descriptions
• Include parameter validation in your tools
• Handle tool call errors gracefully

Code Generation

Mistral's Codestral models excel at code generation across 80+ programming languages:

Fill-in-the-Middle (FIM)

providers:
  - id: mistral:codestral-latest
    config:
      temperature: 0.2
      max_tokens: 2000

tests:
  - vars:
      prompt: |
        <fim_prefix>def calculate_fibonacci(n):
            if n <= 1:
                return n
        <fim_suffix>

        # Test the function
        print(calculate_fibonacci(10))
        <fim_middle>
    assert:
      - type: contains
        value: 'fibonacci'

Code Generation Examples

tests:
  - description: 'Python API endpoint'
    vars:
      prompt: 'Create a FastAPI endpoint that accepts a POST request with user data and saves it to a database'
    assert:
      - type: contains
        value: '@app.post'
      - type: contains
        value: 'async def'

  - description: 'React component'
    vars:
      prompt: 'Create a React component for a user profile card with name, email, and avatar'
    assert:
      - type: contains
        value: 'export'
      - type: contains
        value: 'useState'

Complete Working Examples

Example 1: Multi-Model Comparison

description: 'Compare reasoning capabilities across Mistral models'

providers:
  - mistral:magistral-medium-latest
  - mistral:magistral-small-latest
  - mistral:mistral-large-latest
  - mistral:mistral-small-latest

prompts:
  - 'Solve this step by step: {{problem}}'

tests:
  - vars:
      problem: "A company has 100 employees. 60% work remotely, 25% work hybrid, and the rest work in office. If remote workers get a $200 stipend and hybrid workers get $100, what's the total monthly stipend cost?"
    assert:
      - type: llm-rubric
        value: 'Shows clear mathematical reasoning and arrives at correct answer ($13,500)'
      - type: cost
        threshold: 0.10

Example 2: Code Review Assistant

description: 'AI-powered code review using Codestral'

providers:
  - id: mistral:codestral-latest
    config:
      temperature: 0.3
      max_tokens: 1500

prompts:
  - |
    Review this code for bugs, security issues, and improvements:

    ```{{language}}
    {{code}}
    ```

    Provide specific feedback on:
    1. Potential bugs
    2. Security vulnerabilities  
    3. Performance improvements
    4. Code style and best practices

tests:
  - vars:
      language: 'python'
      code: |
        import subprocess

        def run_command(user_input):
            result = subprocess.run(user_input, shell=True, capture_output=True)
            return result.stdout.decode()
    assert:
      - type: contains
        value: 'security'
      - type: llm-rubric
        value: 'Identifies shell injection vulnerability and suggests safer alternatives'

Example 3: Multimodal Document Analysis

description: 'Analyze documents with text and images'

providers:
  - id: mistral:pixtral-12b
    config:
      temperature: 0.5
      max_tokens: 2000

tests:
  - vars:
      prompt: |
        Analyze this document image and:
        1. Extract key information
        2. Summarize main points
        3. Identify any data or charts
      image_url: 'https://example.com/financial-report.png'
    assert:
      - type: llm-rubric
        value: 'Accurately extracts text and data from the document image'
      - type: length
        min: 200

Authentication & Setup

Environment Variables

# Required
export MISTRAL_API_KEY="your-api-key-here"

# Optional - for custom endpoints
export MISTRAL_API_BASE_URL="https://api.mistral.ai/v1"
export MISTRAL_API_HOST="api.mistral.ai"

Getting Your API Key

1. Visit console.mistral.ai
2. Sign up or log in to your account
3. Navigate to API Keys section
4. Click Create new key
5. Copy and securely store your key

:::warning Security Best Practices

• Never commit API keys to version control
• Use environment variables or secure vaults
• Rotate keys regularly
• Monitor usage for unexpected spikes

:::

Performance Optimization

Model Selection Guide

Use Case	Recommended Model	Why
Cost-sensitive apps	mistral-small-latest	Best price/performance ratio
Complex reasoning	magistral-medium-latest	Step-by-step thinking
Code generation	codestral-latest	Specialized for programming
Vision tasks	pixtral-12b	Multimodal capabilities
High-volume production	mistral-medium-latest	Balanced cost and quality

Context Window Optimization

providers:
  - id: mistral:magistral-medium-latest
    config:
      max_tokens: 8000 # Leave room for 32k input context
      temperature: 0.7

Cost Management

# Monitor costs across models
defaultTest:
  assert:
    - type: cost
      threshold: 0.05 # Alert if cost > $0.05 per test

providers:
  - id: mistral:mistral-small-latest # Most cost-effective
    config:
      max_tokens: 500 # Limit output length

Troubleshooting

Common Issues

Authentication Errors

Error: 401 Unauthorized

Solution: Verify your API key is correctly set:

echo $MISTRAL_API_KEY
# Should output your key, not empty

Rate Limiting

Error: 429 Too Many Requests

Solutions:

• Implement exponential backoff
• Use smaller batch sizes
• Consider upgrading your plan

# Reduce concurrent requests
providers:
  - id: mistral:mistral-large-latest
    config:
      timeout: 30000 # Increase timeout

Context Length Exceeded

Error: Context length exceeded

Solutions:

• Truncate input text
• Use models with larger context windows
• Implement text summarization for long inputs

providers:
  - id: mistral:mistral-medium-latest # 128k context
    config:
      max_tokens: 4000 # Leave room for input

Model Availability

Error: Model not found

Solution: Check model names and use latest versions:

providers:
  - mistral:mistral-large-latest # ✅ Use latest
  # - mistral:mistral-large-2402  # ❌ Deprecated

Debugging Tips

1. Enable debug logging:

   export DEBUG=promptfoo:*
   

2. Test with simple prompts first:

   tests:
     - vars:
         prompt: 'Hello, world!'
   

3. Check token usage:

   tests:
     - assert:
         - type: cost
           threshold: 0.01
   

Getting Help

• Documentation: docs.mistral.ai
• Community: Discord
• Support: support@mistral.ai
• Status: status.mistral.ai

Working Examples

Ready-to-use examples are available in our GitHub repository:

📋 Complete Mistral Example Collection

Run any of these examples locally:

npx promptfoo@latest init --example mistral

Individual Examples:

• AIME2024 Mathematical Reasoning - Evaluate Magistral models on advanced mathematical competition problems
• Model Comparison - Compare reasoning across Magistral and traditional models
• Function Calling - Demonstrate tool use and function calling
• JSON Mode - Structured output generation
• Code Generation - Multi-language code generation with Codestral
• Reasoning Tasks - Advanced step-by-step problem solving
• Multimodal - Vision capabilities with Pixtral

Quick Start

# Try the basic comparison
npx promptfoo@latest eval -c https://raw.githubusercontent.com/promptfoo/promptfoo/main/examples/mistral/promptfooconfig.comparison.yaml

# Test mathematical reasoning with Magistral models
npx promptfoo@latest eval -c https://raw.githubusercontent.com/promptfoo/promptfoo/main/examples/mistral/promptfooconfig.aime2024.yaml

# Test reasoning capabilities
npx promptfoo@latest eval -c https://raw.githubusercontent.com/promptfoo/promptfoo/main/examples/mistral/promptfooconfig.reasoning.yaml

:::tip Contribute Examples

Found a great use case? Contribute your example to help the community!

:::