Model Royale is a web-based tool that lets developers, AI researchers, and enthusiasts compare outputs from multiple large language models side by side by submitting a single prompt. It supports models from OpenAI, Google Gemini, and Anthropic Claude in one unified interface.
How It Works
- Select which models to compare from the available provider options including OpenAI (GPT-4o, GPT-4), Google (Gemini models), and Anthropic (Claude models). Multiple models can be selected for a single comparison run across different providers simultaneously.
- Enter API keys for each provider whose models were selected. Keys are used client-side only and are sent directly to each provider without being stored or intercepted by any server during the comparison session.
- Type a prompt and submit it to all selected models at once. Responses appear side by side showing each model's output rendered as formatted text, along with token usage and response time metrics for direct comparison.
Key Capabilities
- Multi-provider comparison across OpenAI, Google Gemini, and Anthropic Claude in a single browser session without switching between different chat interfaces or provider dashboards.
- Real-time streaming responses with per-model token count and timing information displayed alongside the generated text for each model in the comparison.
- Client-side only architecture. API keys and prompts are sent directly from the browser to each AI provider. No data is persisted on external servers or stored after the session ends.
- Dark-themed interface with side-by-side result cards that show model identity, response text rendered from markdown, and usage statistics for every model tested.
Use Cases
- Evaluate which model produces the best output for a specific task or domain before committing to a provider or integration path in your application.
- Compare model behavior, output quality, and response characteristics across different providers during development and experimentation work.
- Demonstrate differences in model reasoning, creativity, and factual accuracy for educational workshops or technical presentations and training sessions.
- Test prompt effectiveness across multiple models to understand how different LLMs interpret the same instructions and which formulation yields the best result.
Intended Users
- Developers evaluating LLM providers for integration into their applications who need a quick side-by-side comparison without switching between multiple provider dashboards.
- AI researchers and hobbyists experimenting with model capabilities, output quality, and reasoning differences across different providers.
- Technical educators and workshop facilitators demonstrating model behavior differences in classroom or corporate training settings.
Privacy and Security
- No API keys or prompts are stored on any server. All communication goes directly from the browser to the selected AI provider without intermediary persistence.
- The application is a static client-side React application with no backend data persistence layer for user queries or API credentials.
