Run a Face-Off
Paste a plain-language task, run it across a default set of top models, and get a rubric-ranked verdict with a shareable proof page.
1. Open Face-Off
Go to app.redcrown.ai/faceoff or click Face-Off in the app nav. You can also reach it from the call-to-action on the home screen.
Face-Off is available in the UI only. There is no dedicated redcrown CLI command or MCP tool for it today; the guide below covers the UI flow.
2. Paste your task
Type or paste the plain-language instruction you would send any LLM. For example: Write a Python function that merges two sorted lists. No labeled examples or ground-truth answers are required.
Face-Off uses an AI judge to score outputs, so the task can be anything a language model can respond to: code generation, summarization, rewriting, question answering, and so on.
3. Select models
The model list reflects what your workspace can actually run. Connect a provider under Advanced then Models and keys, and the Face-Off offers that provider's models: Claude Haiku 4.5 and Claude Sonnet 5 for Anthropic, and a curated set of top models for OpenRouter. Connect more than one and the Face-Off spans them, which is the most useful comparison it can make. Deselect any model you do not want to include.
No key needed to start. With nothing connected, RedCrown funds the run against its own curated OpenRouter set, and the label above the model list says so. That is a real run on real models, not stub outputs, and it produces a shareable proof. Connect your own key when you want the comparison to cover the models you actually use.
The judge follows the same rule. The AI judge runs on a provider you can reach. If everything you have connected comes from one lab, the judge will come from that lab too, and the report says so in a disclosure line, because a grader scoring its own family is worth knowing about. Connecting a second provider gives you an independent judge.
4. Choose who judges
Select one of three options under Who judges this?
- AI only , the AI judge scores each output immediately and the ranked report is ready as soon as the run finishes.
- AI + human , the AI judge runs first, then a blind reviewer link is minted so a human expert can vote on the contested cases.
- Human only , no AI scoring; a blind reviewer link is minted and the final verdict waits for human votes.
For AI + human and human only, the existing magic-link reviewer portal is used. See Reviewer portal for details on sending and managing reviewer links.
5. Set a consistency band (optional)
Enter a repeat count to run each model N times on the same task. RedCrown reports the mean score and run-to-run standard deviation for each candidate, giving you a consistency band instead of a single-trial result. Higher N means more API calls and proportionally higher spend.
6. Contribute to the model index (optional)
Tick Help build a public model index to share anonymized win and score signals from this run. Your prompts, inputs, and outputs stay private, only the aggregate result (which model won, per-model scores, and a coarse task category) is recorded. It is off by default and opt-in per run.
7. Run and read the verdict
Click Run Face-Off. RedCrown fans the task across every selected model and the AI judge scores each output against three criteria: correctness, completeness, and follows-instructions. Each criterion is scored 1 to 10.
How the winner is picked. The report ranks candidates by score, then breaks ties by cost (cheapest that clears the quality bar wins). This is a model judgment, not ground truth. Because there is no reference answer, the AI judge can disagree with a human expert, and different runs may score differently if the model is non-deterministic. Treat the verdict as a strong signal, not a certified measurement.
To get ground-truth-backed results, bring labeled examples using Prove a task or Bring your own data.
Last verified .