Benchmark LLMs across quality, cost and speed. AI models judge each other, humans validate.
Agent Clash is a free, web-based AI model evaluation platform that benchmarks large language models (LLMs) across quality, cost and speed. Run the same prompt across multiple models, datasets and iterations. Independent AI judges score response quality while Agent Clash tracks latency, cost and output length — giving you objective, reproducible rankings based on your real constraints.
The platform supports multiple evaluation runs, parallel datasets, and a Borda Count aggregation system. AI models judge each other, humans validate. Designed for developers, researchers, and AI enthusiasts who want transparent, data-driven benchmarks.
The evaluation process follows a structured pipeline designed for fairness and statistical rigor:
Your prompt is sent simultaneously to all selected AI models via the OpenRouter API. Each model generates its response independently, with the same temperature and token settings. When multiple datasets are provided, each model processes each data variant separately.
Each model's response is evaluated by independent AI judges. The evaluation is blind: judges do not know which model produced which response. They only see anonymized labels ("Response A", "Response B", etc.) and rank all responses from best to worst based on quality, accuracy, and relevance.
Three additional elite judges (GPT-5.2, Claude Opus 4.5, and Gemini 3.0 Pro) perform a second blind evaluation. Their votes carry 2x weight in the final ranking, acting as a quality anchor and tie-breaker. This Supreme Court layer adds an extra level of rigor to the results.
All judge rankings are aggregated using the Borda Count method, a ranked voting system where each position earns points (1st place receives N points, 2nd place N-1, and so on). The model with the highest total score wins. This method is resistant to outlier votes and produces fair, balanced results.
Each evaluation produces a comprehensive report including the full ranking matrix (every judge by every model), final Borda scores, response times, costs, and all raw model responses. When running multiple iterations or datasets, an aggregate summary provides average scores, rank distributions, standard deviations, win rates, consistency patterns, and cost-performance tradeoffs.
Agent Clash supports models from the following major AI providers, all accessible through the OpenRouter API:
New models are added regularly as they become available on OpenRouter.
What is Agent Clash?
Agent Clash is a free AI model evaluation platform. It lets you compare large language models like GPT, Claude, Gemini, Llama, and Mistral side by side on your own prompts. Independent AI judges score each response, and the platform produces statistically robust rankings across multiple runs and datasets.
How does Agent Clash evaluate AI models?
You submit a prompt and select which AI models to test. All models receive the same prompt simultaneously. Then, independent AI judges (separate LLM instances acting as evaluators) score each response on criteria like accuracy, reasoning, depth, and clarity. You can run multiple iterations and use parallel datasets for statistically robust results.
Is Agent Clash free to use?
Yes. Agent Clash is a free platform. You need your own OpenRouter API key to access the underlying AI models. The cost of each evaluation depends on the models selected and the number of tokens processed, typically a few cents per battle.
What is blind evaluation?
In a blind evaluation, the AI judges do not know which model produced which response. Responses are presented as anonymized labels (Response A, Response B, etc.), preventing any bias toward or against specific models. This ensures that scores reflect actual response quality, not model reputation.
What is the Supreme Court feature?
Supreme Court mode adds three elite AI judges (GPT-5.2, Claude Opus 4.5, and Gemini 3.0 Pro) who perform a second blind evaluation. Their votes carry double weight in the final ranking, serving as a quality anchor and tie-breaker for more decisive results.
What is the Borda Count scoring method?
The Borda Count is a ranked voting system used to aggregate judge rankings. Each position earns points: first place receives N points, second place N-1, and so on. The model with the highest total score wins. This method is mathematically resistant to outlier votes and produces balanced, fair results.
What AI models does Agent Clash support?
Agent Clash supports models from OpenAI (GPT), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral AI (Mistral), and Cohere (Command). New models are added regularly as they become available on the OpenRouter API.
Is my API key stored?
If you use Agent Clash without an account, your API key is only stored locally in your browser. If you create an account, your API key is stored encrypted on our servers for convenience. You can delete it at any time from your account settings.
Why run multiple evaluation rounds?
AI model responses can vary due to temperature randomness. Running multiple rounds reveals whether a model wins consistently or only intermittently. Combined with parallel datasets, multi-run evaluations provide statistically robust results with standard deviations, win rates, and consistency metrics.
For questions, feedback, or partnerships, reach out at: contact@agent-clash.ai