Mistral AI API Mistral Large Small Codestral Integration

Mistral AI API Integration: Mistral Large, Small, Codestral

Mistral AI is a European LLM provider. Key advantages: GDPR compliance (data processed in EU), competitive pricing, open-source models for local deployment. Codestral is a specialized code model with Fill-in-the-Middle (FIM) support.

Integration via Official SDK

from mistralai import Mistral

client = Mistral(api_key="MISTRAL_API_KEY")

# Basic call
response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Hello"}],
    temperature=0.1,
)
print(response.choices[0].message.content)

# Async
import asyncio

async def async_chat(prompt: str) -> str:
    response = await client.chat.complete_async(
        model="mistral-small-latest",
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

# Streaming
with client.chat.stream(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Long answer"}],
) as stream:
    for event in stream:
        print(event.data.choices[0].delta.content or "", end="")

Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "search_db",
        "description": "Search company database",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string"},
                "limit": {"type": "integer", "default": 10},
            },
            "required": ["query"]
        }
    }
}]

response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Find information about client Ivanov"}],
    tools=tools,
    tool_choice="auto",
)

Codestral for Code (FIM)

# Fill-in-the-Middle — code completion between prefix and suffix
fim_response = client.fim.complete(
    model="codestral-latest",
    prompt="def calculate_discount(price: float,",
    suffix=") -> float:\n    return discounted_price",
    temperature=0,
    max_tokens=256,
)
print(fim_response.choices[0].message.content)

# Code generation
code_response = client.chat.complete(
    model="codestral-latest",
    messages=[{"role": "user", "content": "Write a Python function to parse CSV with error handling"}],
)

Embeddings

response = client.embeddings.create(
    model="mistral-embed",
    inputs=["First document", "Second document"],
)
embeddings = [item.embedding for item in response.data]

Cost of Mistral (2025)

Local Deployment via Ollama

ollama pull mistral:7b
ollama pull codestral:22b

# Mistral via Ollama — OpenAI-compatible API
from openai import OpenAI
local_client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
response = local_client.chat.completions.create(
    model="mistral:7b",
    messages=[{"role": "user", "content": "Hello"}],
)

Timeline

• Basic integration: 0.5–1 day
• Codestral for IDE assistant: 2–3 days
• Local deployment + optimization: 1 week