About Model Kombat
Fair, blind AI model comparison for everyone
Our Mission
Model Kombat was built to solve a fundamental problem in AI evaluation: bias. When comparing AI models, knowing which model produced which output inevitably influences judgment. We created a platform that eliminates this bias through blind, anonymized tournaments where outputs are judged purely on their merit.
How It Works
Model Kombat runs structured tournaments where multiple AI models compete on the same prompts. Each model's output is anonymized with labels like "Model A" or "Model B", ensuring judges evaluate content without knowing its source.
Our platform supports multiple refinement rounds, where models can improve their outputs based on structured critiques. This iterative process reveals not just initial quality, but each model's ability to learn and adapt.
Key Features
- Blind Evaluation: Anonymized outputs ensure unbiased judging
- Multi-Round Refinement: Test how models improve with feedback
- Flexible Judging: Use AI judges, human panels, or both
- Custom Rubrics: Define your own evaluation criteria
- Shareable Results: Invite external judges with share links
Who Uses Model Kombat
Developers
Choose the best model for applications without vendor bias
Researchers
Run controlled experiments comparing model capabilities
Teams
Make data-driven decisions with quantifiable metrics
Questions? Reach out at support@modelkombat.com