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

# Supported Models

> Compatible AI models for use with Rogue evaluations

## Supported Models

Rogue uses LiteLLM for model compatibility, allowing you to use a wide range of AI models from different providers. The following tables show the models we have tested with Rogue.

## ✅ Successfully Tested Models

### OpenAI

* gpt-5
* gpt-5-mini
* gpt-5-nano
* openai/gpt-4.1
* openai/gpt-4.1-mini
* openai/gpt-4.5-preview
* openai/gpt-4o
* openai/gpt-4o-mini
* openai/o4-mini

### Gemini (Vertex AI or Google AI)

* gemini-2.5-flash
* gemini-2.5-pro

### Anthropic

* anthropic/claude-3-5-sonnet-latest
* anthropic/claude-3-7-sonnet-latest
* anthropic/claude-4-sonnet-latest

## ❌ Unsupported Models

### OpenAI

* openai/o1 (including mini) - Not compatible with Rogue's evaluation framework

### Gemini (Vertex AI or Google AI)

* gemini-2.5-flash - Partial support only, may have limitations

## Environment Variables

Rogue uses LiteLLM for model management, so you can set API keys for various providers using standard environment variables:

```env theme={null}
# OpenAI
OPENAI_API_KEY="sk-..."

# Anthropic
ANTHROPIC_API_KEY="sk-..."

# Google (for Gemini)
GOOGLE_API_KEY="..."

# Additional providers supported by LiteLLM
AZURE_API_KEY="..."
COHERE_API_KEY="..."
# ... and many more
```

## Model Selection

When configuring Rogue, you can specify models in LiteLLM format:

* `openai/gpt-4o-mini` for OpenAI models
* `anthropic/claude-3-5-sonnet-latest` for Anthropic models
* `gemini-2.5-pro` for Google models

## Performance Considerations

* **GPT-4o-mini**: Excellent balance of performance and cost for most evaluations
* **Claude-3.5-Sonnet**: Great for complex reasoning and nuanced evaluations
* **Gemini-2.5-Pro**: Strong performance for analysis and reporting tasks

## Testing New Models

If you want to test Rogue with a model not listed here:

1. Ensure the model is supported by LiteLLM
2. Set the appropriate API key
3. Use the correct model identifier format
4. Test with a simple evaluation first

Models that support function calling and structured outputs generally work best with Rogue's evaluation framework.