An individual tested Anthropic's Claude Opus 5.5 and Sonnet models to understand their reasoning effort policies and their impact on bug fixes. The user discovered that their settings were consistently using higher effort levels (xhigh and max) than recommended by Anthropic, even for subagent messages. Despite this deviation, the user found no immediate evidence of wasted resources, as Anthropic's published benchmarks are based on single-shot tasks, unlike their own long, interactive sessions. To determine if lower effort levels would negatively affect performance, the user designed an experiment with Codex and Gemini, focusing on statistical power and using real bug fixes with associated tests. AI
IMPACT Understanding optimal model settings can lead to more efficient AI usage and cost savings for developers.
RANK_REASON User-provided analysis and experimental design regarding model settings, not a primary release or research paper.
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