> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Mistral

> Learn how to use Mistral models in Agno.

Mistral is a platform for providing endpoints for Large Language models.
See their library of models [here](https://docs.mistral.ai/getting-started/models/models_overview/).

We recommend experimenting to find the best-suited model for your use-case. Here are some general recommendations:

* `codestral` model is good for code generation and editing.
* `mistral-large-latest` model is good for most use-cases.
* `open-mistral-nemo` is a free model that is good for most use-cases.
* `pixtral-12b-2409` is a vision model that is good for OCR, transcribing documents, and image comparison. It is not always capable at tool calling.

Mistral has tier-based rate limits. See the [docs](https://docs.mistral.ai/deployment/laplateforme/tier/) for more information.

## Authentication

Set your `MISTRAL_API_KEY` environment variable. Get your key from [here](https://console.mistral.ai/api-keys/).

<CodeGroup>
  ```bash Mac theme={null}
  export MISTRAL_API_KEY=***
  ```

  ```bash Windows theme={null}
  setx MISTRAL_API_KEY ***
  ```
</CodeGroup>

## Example

Use `Mistral` with your `Agent`:

<CodeGroup>
  ```python agent.py theme={null}
  import os

  from agno.agent import Agent, RunResponse
  from agno.models.mistral import MistralChat

  mistral_api_key = os.getenv("MISTRAL_API_KEY")

  agent = Agent(
      model=MistralChat(
          id="mistral-large-latest",
          api_key=mistral_api_key,
      ),
      markdown=True
  )

  # Print the response in the terminal
  agent.print_response("Share a 2 sentence horror story.")

  ```
</CodeGroup>

<Note> View more examples [here](../examples/models/mistral). </Note>

## Params

<Snippet file="model-mistral-params.mdx" />

`MistralChat` is a subclass of the [Model](/reference/models/model) class and has access to the same params.