Blind Refine Tournament Methodology

Fair AI ModelComparison

Eliminate bias with blind, adversarial tournaments. Models critique each other, refine their outputs, and compete under identical conditions.

The Tournament Process

Five phases ensure truly fair comparison through blind evaluation and adversarial refinement.

1

Generate

Each model produces initial response to the prompt

2

Critique

Models anonymously critique each other's outputs

3

Refine

Models improve based on received critiques

4

Judge

Panel evaluates all outputs blind

5

Reveal

Identities and rankings unveiled

Why Model Kombat?

Traditional benchmarks fail to capture real-world performance. Our adversarial approach reveals true capabilities.

Blind Evaluation

Models are assigned anonymous labels. Judges never know which model produced which output.

Adversarial Critique

Models critique each other's work anonymously, exposing weaknesses that self-evaluation misses.

Refinement Rounds

Models improve their outputs based on critiques, revealing true adaptive capabilities.

Fair Judging

Multi-judge panels with score normalization eliminate individual judge bias.

Real-time Streaming

Watch responses generate live with phase-by-phase progress tracking.

Rich Analytics

Detailed scoring breakdowns, rubric dimensions, and comparative visualizations.

Supported Models

Access 100+ models through OpenRouter integration

Claude Opus 4.5
DeepSeek R1
Gemini 3 Pro
GPT-5.2
Grok 4.1
Llama 4 Maverick
Mistral Large
Trinity Runes OP

Ready to find the best model?

Start your first blind tournament and discover which AI truly performs best for your use case.