Cheaper AI Model Ties Pricier Rival on What Actually Matters: Zero Fatal Errors

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A developer ran a 29-question order-reading exam on two AI models — Claude Haiku 4.5 and the roughly three-times-costlier Claude Sonnet 5 — to compare their practical accuracy. The expensive model answered all 28 executed questions correctly, while the cheaper model scored 28 out of 29, with the one miss being a cautious clarifying question rather than a harmful wrong answer. The author argues that severity of error, not raw score, is the right evaluation metric, meaning both models effectively tied at zero fatal errors. A key technical finding was that output variability due to model temperature made single-run scores unreliable, with the same question producing different answers across runs. The developer concludes that teams should pin temperature to zero for consistent results and default to the cheaper model unless the pricier one demonstrates a meaningful, severity-level advantage.

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