Mistral

Mistral AI focuses on efficiency and coding capabilities. Their "Mixture of Experts" (MoE) architecture (Mixtral) changed the game.

When to Use

• High-Performance European & Multilingual LLMs: Mistral Large, Mistral NeMo, and Codestral for code intelligence and multilingual reasoning.
• Efficient Function Calling & JSON Enforcement: Executing structured agentic workflows with low latency.
• On-Premise & Open-Weights Self-Hosting: Deploying open weights (Mistral-7B, Mixtral 8x7B/8x22B) with vLLM or Ollama.
• High-Speed Code Completion: Integrating Codestral into IDEs with Fill-in-the-Middle (FIM) capabilities.

Quick Start

from mistralai import Mistral

client = Mistral(api_key="your-mistral-api-key")

response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Explain Mixture of Experts (MoE) in two sentences."}]
)

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

Core Concepts

Chat Completion with Mistral Python Client

Calling Mistral Large with structured configuration:

import os
from mistralai import Mistral

client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))

response = client.chat.complete(
    model="mistral-large-latest",
    messages=[
        {"role": "system", "content": "You are an expert security engineer."},
        {"role": "user", "content": "Analyze the security implications of using JWT in local storage."}
    ],
    temperature=0.2,
    max_tokens=1024
)

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

Tool Use & Function Calling

Binding tools to Mistral for multi-turn agent execution:

import json
from mistralai import Mistral

client = Mistral()

tools = [
    {
        "type": "function",
        "function": {
            "name": "lookup_dns_records",
            "description": "Query DNS records for a given domain name.",
            "parameters": {
                "type": "object",
                "properties": {
                    "domain": {"type": "string", "description": "The domain name, e.g. example.com"},
                    "record_type": {"type": "string", "enum": ["A", "AAAA", "MX", "TXT"]}
                },
                "required": ["domain", "record_type"]
            }
        }
    }
]

response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Check the MX records for github.com"}],
    tools=tools,
    tool_choice="auto"
)

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

Fill-in-the-Middle (FIM) with Codestral

Completing code between prefix and suffix markers:

from mistralai import Mistral

client = Mistral()

prefix = '''def calculate_tax(income: float) -> float:
    # Validate input
    if income < 0:
        raise ValueError('Income cannot be negative')
'''
suffix = '''    return tax_amount
'''

response = client.fim.complete(
    model="codestral-latest",
    prompt=prefix,
    suffix=suffix,
    temperature=0.1
)

print("Infilled Code:\n", response.choices[0].message.content)

Common Patterns

Code Generation with Codestral FIM (Fill-in-the-Middle)

Problem: Generating code completions that seamlessly fit between existing prefix and suffix code snippets.

Solution: Use Codestral's native FIM endpoint:

response = client.fim.complete(
    model="codestral-latest",
    prompt="def binary_search(arr, target):\n    left, right = 0, len(arr) - 1\n",
    suffix="\n    return -1"
)

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

Best Practices

Do:

• Target codestral-latest for coding tasks and Fill-in-the-Middle (FIM) in-editor completions.
• Use response_format={"type": "json_object"} when machine-readable JSON is strictly required.
• Set temperature=0.1 for code generation and factual extraction tasks.
• Leverage Mixtral Mixture-of-Experts (MoE) architectures for high throughput with reduced active parameter counts.

Don't:

• Provide unstructured tool descriptions; define strict JSON schemas with clear parameter types.
• Hardcode MISTRAL_API_KEY in scripts; load from environment variables.
• Use large general models when specialized compact models like ministral-8b or mistral-nemo suffice.

Troubleshooting

References

• Mistral AI Documentation