# Mistral
URL: /reference/llm-providers/mistral

Mistral AI provides a range of powerful language models designed for professional use cases and complex reasoning tasks. This page covers the Mistral models available in Tambo, their capabilities, and how to configure them.

<Callout type="warning" title="Known Rendering Issues">
  Mistral models (Large 2.1 and Medium 3) may inconsistently follow rendering
  instructions, similar to Gemini models. Try clarifying prompt structure if you
  encounter formatting issues. See [Labels](/reference/llm-providers/labels) for
  more details.
</Callout>

## Available Models

Tambo supports two Mistral models for high-performance production use.

### Mistral Medium 3

**Status:** Known Issues
**API Name:** `mistral-medium-2505`
**Context Window:** 128,000 tokens

Designed to be frontier-class, particularly excelling in categories of professional use. This model provides a balance of power and versatility for production workloads.

**Best for:**

* Professional applications requiring reliable performance
* Long-form content generation and analysis
* Multi-document reasoning with large context windows
* Production deployments where consistency matters

**Notes:** May occasionally resist rendering as requested. Try clarifying instructions (e.g., "Return a bulleted list only"). Outputs may have formatting quirks when structure is important.

**Provider Documentation:** [Mistral AI - Mistral Medium 3](https://mistral.ai/news/mistral-medium-3)

***

### Mistral Large 2.1

**Status:** Known Issues
**API Name:** `mistral-large-latest`
**Context Window:** 128,000 tokens

Mistral's top-tier large model for high-complexity tasks, with the latest version released in November 2024. This model represents Mistral's most capable offering for demanding workloads.

**Best for:**

* High-complexity reasoning and analysis
* Advanced code generation and review
* Multi-turn conversations requiring context retention
* Tasks demanding maximum model capability

**Notes:** Similar to Medium 3, may inconsistently follow rendering instructions. Validate outputs where structure is critical.

**Provider Documentation:** [Mistral AI - Pixtral Large](https://mistral.ai/news/pixtral-large)

## Configuration

### Setting Up Mistral in Your Project

1. Navigate to your project in the Tambo dashboard
2. Go to **Settings** → **LLM Providers**
3. Add or configure your Mistral API credentials
4. Select your preferred [Mistral model](#available-models)
5. Adjust token limits and parameters as needed
6. Click **Save** to apply your configuration

### Custom Parameters

Mistral models support standard LLM parameters like temperature, max tokens, and more. Configure these in the dashboard under [**Custom LLM Parameters**](/guides/setup-project/llm-provider).

For detailed information on available parameters, see [Custom LLM Parameters](/guides/setup-project/llm-provider).

## Model Comparison

| Model             | Context Window | Status       | Best Use Case             |
| ----------------- | -------------- | ------------ | ------------------------- |
| Mistral Medium 3  | 128K tokens    | Known Issues | Professional applications |
| Mistral Large 2.1 | 128K tokens    | Known Issues | High-complexity tasks     |

## Best Practices

### Choosing the Right Model

* **Use [Mistral Medium 3](#mistral-medium-3)** for professional applications with large context windows
* **Reserve [Mistral Large 2.1](#mistral-large-2-1)** for the most demanding tasks requiring maximum capability

### Handling Rendering Issues

If you encounter formatting inconsistencies with [Medium 3](#mistral-medium-3) or [Large 2.1](#mistral-large-2-1):

1. **Clarify instructions** - Be explicit about desired output format
2. **Use structured prompts** - Provide clear examples of expected structure
3. **Validate outputs** - Add checks for critical formatting requirements
4. **Test thoroughly** - Run a prompt suite to verify behavior in your workload

For production-critical formatting, consider using [**Tested**](/reference/llm-providers/labels) models and validating outputs. See [Labels](/reference/llm-providers/labels) for more guidance.

## Troubleshooting

**Model not appearing in dashboard?**

* Verify your Mistral API key is [configured correctly](#setting-up-mistral-in-your-project)
* Check that your Tambo Cloud instance is up to date
* Ensure you have proper permissions for your project

**Inconsistent formatting in responses?**

* This is a [known issue](#available-models) with [Medium 3](#mistral-medium-3) and [Large 2.1](#mistral-large-2-1) models
* Try being more explicit in your prompt instructions
* Consider adjusting prompt instructions if formatting is critical
* See [Labels](/reference/llm-providers/labels) for detailed behavior notes

**High token usage?**

* [Mistral Large 2.1](#mistral-large-2-1) and [Medium 3](#mistral-medium-3) have 128K context windows
* Monitor your input length and conversation history
* Use token limits in [dashboard settings](#setting-up-mistral-in-your-project) to control costs
* Consider using token limits in dashboard settings to control costs

## See Also

* [Labels](/reference/llm-providers/labels) - Understanding model status labels and observed behaviors
* [Custom LLM Parameters](/guides/setup-project/llm-provider) - Configuring model parameters
* [Reasoning Models](/reference/llm-providers/reasoning-models) - Advanced reasoning capabilities