---
name: mistral-d9e4041f7550
description: Expert Mistral AI assistance covering Mistral Large/Small, Codestral, Pixtral, function calling, and local deployment via Ollama/vLLM. Use when deploying high-efficiency open foundation models.
---

# 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

```python
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:

```python
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:

```python
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:

```python
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:

```python
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

| Error                               | Cause                                              | Solution                                                        |
| :---------------------------------- | :------------------------------------------------- | :-------------------------------------------------------------- |
| `MistralAPIError: 401 Unauthorized` | Invalid or unset API key.                          | Pass valid key via `api_key` or export `MISTRAL_API_KEY`.       |
| `429 Too Many Requests`             | Monthly quota or concurrent request limit reached. | Monitor usage in Mistral Console or self-host weights via vLLM. |
| `Model not found error`             | Deprecated model tag used in API call.             | Use active aliases: `mistral-large-latest`, `codestral-latest`. |

## References

- [Mistral AI Documentation](https://docs.mistral.ai/)


---
Source attribution: G1Joshi
Original project: https://github.com/G1Joshi/Agent-Skills/blob/6a74cf03a8a86dc7d993ead3091b41123dbd0181/skills/ai-ml/mistral/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 G1Joshi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: G1Joshi
Original project: https://github.com/G1Joshi/Agent-Skills/blob/6a74cf03a8a86dc7d993ead3091b41123dbd0181/skills/ai-ml/mistral/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API, Backend
Tools: Mistral
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 4e7db12ae46f949c20fe4a6a271795908cd3cbd510176fd977ec749970001504