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Model disagreement helps spot inputs that cause uncertainty in predictions. By monitoring metrics like ensemble variance or margin, you can identify the riskiest 1-2% of cases

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When models disagree, it highlights cases with uncertain predictions that could be risky. Tracking metrics like ensemble variance or prediction margin helps spot these tricky inputs

Disagreement between AI models may sound counterintuitive, but it can catch silent hallucinations—errors that slip past unnoticed—in high-stakes tasks

OpenRouter acts like a simple switchboard, sending your request to one model at a time. It’s quick and easy but doesn’t keep context between calls, which can interrupt smooth conversations or require extra setup for chaining responses

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