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Overview

Mistral AI provides open-weight and commercial models with strong performance, efficient inference, and competitive pricing. Access Mistral through Portkey for European-hosted AI with excellent multilingual capabilities. Base URL: https://api.mistral.ai/v1

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Supported Features

  • ✅ Chat Completions
  • ✅ Streaming
  • ✅ Embeddings
  • ✅ Function Calling
  • ✅ JSON Mode
  • ✅ Fill-in-the-middle (FIM)
  • ❌ Vision (not yet available)
  • ❌ Image Generation
  • ❌ Fine-tuning

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Quick Start

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Chat Completions

from portkey_ai import Portkey

client = Portkey(
    provider="mistral-ai",
    Authorization="***"  # Your Mistral API key
)

response = client.chat.completions.create(
    model="mistral-large-latest",
    messages=[
        {"role": "user", "content": "Explain the Mistral models"}
    ]
)

print(response.choices[0].message.content)

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Streaming

stream = client.chat.completions.create(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Write a poem about Paris"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

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Available Models

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Commercial Models

ModelContextDescriptionBest For
mistral-large-latest128KMost capable Mistral modelComplex reasoning, multilingual
mistral-large-2411128KLatest Mistral LargeProduction applications
mistral-large-2407128KJuly 2024 versionStable release
mistral-medium-latest32KBalanced performanceGeneral purpose
mistral-small-latest32KFast and efficientSimple tasks, high volume

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Open-Weight Models

ModelContextDescription
open-mistral-nemo128KLatest open model
open-mixtral-8x22b64KMixture of Experts (MoE)
open-mixtral-8x7b32KEfficient MoE
open-mistral-7b32KCompact model

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Specialized Models

ModelTypeDescription
codestral-latestCodeCode generation
codestral-mamba-latestCodeEfficient code model
mistral-embedEmbeddingsText embeddings (1024 dims)
Mistral excels at:
  • Multilingual tasks (French, English, Spanish, German, Italian)
  • Code generation with Codestral
  • Efficient inference with MoE architecture
  • European data residency (GDPR compliant)
  • Instruction following and function calling

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Configuration Options

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Headers

client = Portkey(
    provider="mistral-ai",
    Authorization="***"  # Bearer token format
)
HeaderDescriptionRequired
AuthorizationMistral API key (Bearer token)Yes

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Advanced Features

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Function Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto"
)

if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

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JSON Mode

Force JSON output: 
response = client.chat.completions.create(
    model="mistral-large-latest",
    messages=[{
        "role": "user",
        "content": "List 3 French cities with their populations"
    }],
    response_format={"type": "json_object"}
)

import json
result = json.loads(response.choices[0].message.content)
print(result)

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System Prompts

response = client.chat.completions.create(
    model="mistral-large-latest",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful Python programming expert. Always provide working code with explanations."
        },
        {
            "role": "user",
            "content": "How do I read a CSV file?"
        }
    ]
)

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Fill-in-the-Middle (FIM)

Special mode for code completion: 
client = Portkey(
    provider="mistral-ai",
    Authorization="***",
    mistral_fim_completion="true"  # Enable FIM mode
)

response = client.completions.create(
    model="codestral-latest",
    prompt="def fibonacci(n):\n    # Complete this function\n    "
)

print(response.choices[0].text)

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Embeddings

response = client.embeddings.create(
    model="mistral-embed",
    input="Mistral AI provides European-hosted AI models"
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
Batch embeddings: 
response = client.embeddings.create(
    model="mistral-embed",
    input=[
        "First document",
        "Second document",
        "Third document"
    ]
)

for i, item in enumerate(response.data):
    print(f"Document {i}: {len(item.embedding)} dimensions")

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Fallback Configuration

Fallback to OpenAI: 
config = {
    "strategy": {"mode": "fallback"},
    "targets": [
        {
            "provider": "mistral-ai",
            "api_key": "***",
            "override_params": {"model": "mistral-large-latest"}
        },
        {
            "provider": "openai",
            "api_key": "sk-***",
            "override_params": {"model": "gpt-4o"}
        }
    ]
}

client = Portkey().with_options(config=config)

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Load Balancing

Balance between Mistral models: 
config = {
    "strategy": {"mode": "loadbalance"},
    "targets": [
        {
            "provider": "mistral-ai",
            "api_key": "***",
            "override_params": {"model": "mistral-large-latest"},
            "weight": 0.3
        },
        {
            "provider": "mistral-ai",
            "api_key": "***",
            "override_params": {"model": "mistral-small-latest"},
            "weight": 0.7
        }
    ]
}

client = Portkey().with_options(config=config)

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Error Handling

from portkey_ai.exceptions import (
    RateLimitError,
    APIError,
    AuthenticationError
)

try:
    response = client.chat.completions.create(
        model="mistral-large-latest",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except AuthenticationError as e:
    print(f"Invalid API key: {e}")
except APIError as e:
    print(f"API error: {e}")

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Best Practices

  1. Use latest versions - Model IDs with “latest” get automatic updates
  2. Leverage function calling - Mistral has strong tool use capabilities
  3. Try JSON mode - For structured outputs
  4. Use Codestral - For code-specific tasks
  5. Consider Small for volume - Cost-effective for simple tasks
  6. Enable streaming - Better user experience
  7. Use embeddings - For semantic search and RAG
  8. System prompts - Guide behavior consistently

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Context Windows

ModelContext WindowNotes
mistral-large-latest128K tokensFull documents
mistral-medium-latest32K tokensStandard documents
mistral-small-latest32K tokensStandard documents
open-mistral-nemo128K tokensLong context open model

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European Data Residency

Mistral AI is headquartered in France and offers EU data residency:
  • GDPR compliant by default
  • European infrastructure (Paris, Frankfurt)
  • Data sovereignty for EU customers
  • No data training on customer inputs

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Pricing

Mistral offers competitive pricing with open models:

Mistral Pricing

View detailed pricing for all Mistral models

Function Calling

Advanced function calling

Fallbacks

Fallback configurations

Code Generation

Using Codestral

JSON Mode

Structured outputs

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