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Benchmarks

What AI Debate measures before trusting a consensus

AI Debate is not a claim that four models make an answer correct. It is a workflow for surfacing agreement, preserving disagreement, and showing what still needs verification.

Agreement

Do independent models repeat the same core claim?

Repeated claims are not automatically true, but agreement is a useful signal when the prompt includes enough context and the claim can be checked.

Disagreement

Which assumptions produce different recommendations?

Disagreement is preserved in the result so users can see whether a recommendation depends on risk tolerance, missing data, or model-specific framing.

Latency

How long did the panel take to respond?

Provider response time is part of product quality. A slow but more complete answer may be useful for planning; a quick answer may be better for lightweight drafting.

Human review

What changed after comparing the answers?

The most useful case studies show what a single model missed, what the panel agreed on, and which claims still require outside sources.

Current benchmark posture

The public product reports provider answers, cross-review notes, contradictions, and the final synthesis. The next benchmark layer should publish fixed prompt suites with model versions, response time, consensus changes, and human-reviewed error notes.