What is Agent Clash?

Agent Clash is a free, web-based AI model evaluation platform. Users submit a prompt to multiple large language models simultaneously — including GPT (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta), Mistral, and Command (Cohere). Independent AI judges then evaluate each response blindly. For each prompt, AI dynamically identifies the most relevant evaluation criteria (e.g. code correctness, reasoning depth, factual accuracy) and judges rate each response on those criteria from 1 to 5, with justifications for low scores.

The platform supports multiple evaluation runs and parallel datasets to produce statistically robust, reproducible rankings. Agent Clash uses the Borda Count aggregation method — a ranked voting system resistant to outlier bias — to determine fair, balanced results.

Agent Clash is designed for developers, researchers, prompt engineers, product teams, and AI enthusiasts who want a transparent and data-driven way to benchmark AI model performance.

How Does Agent Clash Work?

  1. Prompt dispatch: Your prompt is sent simultaneously to all selected AI models via the OpenRouter API. Each model generates its response independently with the same settings.
  2. Dynamic criteria generation: An AI model analyzes your prompt and identifies 3-5 evaluation criteria most relevant to the task (e.g. code correctness, clarity of explanation, factual accuracy).
  3. Blind evaluation by AI judges: Independent AI judges evaluate each response without knowing which model produced it. They rank responses and rate each one on every criterion (1-5 scale), with justifications for scores of 3 or below.
  4. Supreme Court review (optional): Three elite judges (GPT-5.2, Claude Opus 4.5, Gemini 3.0 Pro) perform a second blind evaluation with 2x weighted votes, acting as a quality anchor.
  5. Borda Count aggregation: All judge rankings are aggregated using the Borda Count method. First place earns N points, second place N-1, and so on. The highest-scoring model wins.
  6. Reports and analysis: Full scoring matrices, per-criteria score breakdowns (1-5) with judge justifications, response times, costs, win rates, standard deviations, and consistency metrics across runs and datasets.

Supported AI Models

  • OpenAI: GPT-4o, GPT-4o mini, GPT-4.1, o3, o4-mini
  • Anthropic: Claude Opus 4, Claude Sonnet 4, Claude 3.5 Sonnet, Claude 3.5 Haiku
  • Google: Gemini 3.0 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash
  • Meta: Llama 4 Maverick, Llama 4 Scout, Llama 3.3 70B
  • Mistral AI: Mistral Large, Mistral Medium, Mistral Small, Codestral
  • Cohere: Command R, Command R+, Command A

New models are added regularly as they become available on OpenRouter.

Key Features

  • Side-by-side AI model comparison on custom prompts
  • Independent AI judges for impartial evaluation
  • Blind evaluation prevents model-name bias
  • Multi-run evaluations for statistical reliability and consistency analysis
  • Parallel dataset testing for data sensitivity analysis
  • Supreme Court mode with elite judge panel (2x vote weight)
  • Borda Count ranked voting aggregation, resistant to outlier bias
  • Dynamic evaluation criteria tailored to each prompt (AI-generated, 3-5 criteria)
  • Per-criteria scoring (1-5) with judge justifications for low scores
  • Comprehensive reports: scoring matrices, win rates, standard deviations, cost analysis
  • Free and transparent — users provide their own OpenRouter API key

Frequently Asked Questions

Is Agent Clash free?
Yes. Agent Clash is free. You provide your own OpenRouter API key and pay only the model inference costs, typically a few cents per evaluation.
What is blind evaluation?
AI judges do not know which model produced which response. Responses are labeled anonymously (Response A, B, C, etc.) so that scores reflect actual quality, not model reputation.
What is the Supreme Court feature?
Supreme Court mode adds three elite judges (GPT-5.2, Claude Opus 4.5, Gemini 3.0 Pro) whose votes carry double weight, providing an extra layer of evaluation rigor.
What is the Borda Count?
A ranked voting method where each position earns points. First place gets N points, second N-1, etc. The model with the highest total wins. This method is resistant to outlier votes.
Why run multiple evaluation rounds?
AI responses vary due to temperature randomness. Multiple rounds reveal whether a model wins consistently. Combined with parallel datasets, multi-run evaluations produce robust statistics with standard deviations and win rates.
Is my API key stored?
Without an account, your API key stays in your browser only. With an account, it is stored encrypted on our servers and can be deleted at any time.