Mistral AI API

Overview

Mistral AI is a French AI company providing high-quality, cost-efficient language models with EU data residency and GDPR compliance. Their models excel at code generation (Codestral), multilingual tasks, and reasoning. Mistral's API follows OpenAI conventions closely, making integration straightforward.

Setup

# Python
pip install mistralai

# TypeScript/Node
npm install @mistralai/mistralai

export MISTRAL_API_KEY=...

Available Models

mistral-large-latest

mistral-small-latest

codestral-latest

mistral-embed

open-mistral-nemo

Instructions

Basic Chat Completion (Python)

from mistralai import Mistral

client = Mistral(api_key="your_api_key")  # or reads MISTRAL_API_KEY

response = client.chat.complete(
    model="mistral-large-latest",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the difference between async and sync programming."},
    ],
)

print(response.choices[0].message.content)
print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Completion tokens: {response.usage.completion_tokens}")

TypeScript/Node.js

import Mistral from "@mistralai/mistralai";

const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

const response = await client.chat.complete({
  model: "mistral-large-latest",
  messages: [{ role: "user", content: "Hello from TypeScript!" }],
});

console.log(response.choices[0].message.content);

Streaming

from mistralai import Mistral

client = Mistral()

stream = client.chat.stream(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "Write a haiku about programming."}],
)

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

Function Calling

import json
from mistralai import Mistral

client = Mistral()

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_products",
            "description": "Search for products in a catalog",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "max_price": {"type": "number"},
                    "category": {"type": "string"},
                },
                "required": ["query"],
            },
        },
    }
]

messages = [{"role": "user", "content": "Find laptops under $1000"}]

response = client.chat.complete(
    model="mistral-large-latest",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

if response.choices[0].finish_reason == "tool_calls":
    tool_call = response.choices[0].message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    print(f"Function: {tool_call.function.name}, Args: {args}")

    # Add tool result and continue
    messages.append(response.choices[0].message)
    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps([{"name": "ThinkPad X1", "price": 899}]),
    })

    final = client.chat.complete(model="mistral-large-latest", messages=messages)
    print(final.choices[0].message.content)

JSON Mode

from mistralai import Mistral
import json

client = Mistral()

response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {
            "role": "user",
            "content": "Return a JSON object with fields: title, author, year for the book '1984'",
        }
    ],
    response_format={"type": "json_object"},
)

data = json.loads(response.choices[0].message.content)
print(data)  # {"title": "1984", "author": "George Orwell", "year": 1949}

Text Embeddings

from mistralai import Mistral

client = Mistral()

response = client.embeddings.create(
    model="mistral-embed",
    inputs=["Machine learning is transforming industries.", "AI is the future of technology."],
)

embeddings = [item.embedding for item in response.data]
print(f"Embedding dimension: {len(embeddings[0])}")  # 1024

# Compute cosine similarity
import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

similarity = cosine_similarity(embeddings[0], embeddings[1])
print(f"Similarity: {similarity:.3f}")

Codestral for Code Completion

from mistralai import Mistral

client = Mistral()

# Fill-in-the-middle (FIM) — Codestral's signature feature
response = client.fim.complete(
    model="codestral-latest",
    prompt="def fibonacci(n):\n    if n <= 1:\n        return n\n    ",
    suffix="\n\nresult = fibonacci(10)\nprint(result)",
)

print(response.choices[0].message.content)
# Returns the middle code that connects prompt to suffix

# Standard code generation
response = client.chat.complete(
    model="codestral-latest",
    messages=[
        {
            "role": "user",
            "content": "Write a Python class for a rate limiter using token bucket algorithm.",
        }
    ],
)
print(response.choices[0].message.content)

GDPR Compliance Notes

• All API data processed in EU data centers by default.
• Mistral AI is headquartered in Paris, France — subject to EU/GDPR jurisdiction.
• For enterprise data residency guarantees, use Mistral's Azure or GCP deployments.
• No training on user data by default — check your plan's DPA for details.

Guidelines

• Use

  mistral-large-latest

  for complex tasks,

  mistral-small-latest

  for cost savings.
• Codestral is specialized for code and significantly outperforms general models on FIM tasks.
• The

  mistral-embed

  model produces 1024-dimensional vectors.
• Mistral models have strong multilingual performance, especially in French, Spanish, Italian, German, and Portuguese.
• Function calling requires

  tool_choice

  to be set — use

  "auto"

  for model-driven decisions.
• JSON mode requires the system or user prompt to explicitly mention JSON output.