Anthropic appears to be A/B testing reduced effort levels in Claude Code

The thread is less interested in the displayed effort number than in whether Claude Code's higher reasoning modes have become visibly worse in real work. Several developers describe simple edits expanding into long, costly agent detours: container setup, broad repository inspection, and test construction that add nothing when the requested outcome is a single configuration-file change. Others say medium effort now beats high or extra-high effort because the latter is more likely to wander, overthink, or produce confident but wrong work. Some have responded by downgrading expensive subscriptions and using competing models to repair Claude's mistakes. An Anthropic team member says the current experiment only changes how an internal numeric effort value is mapped, not the selected effort or model quality, and points users toward feedback reports for reproducible regressions. That clarification won some appreciation, but it did not settle distrust. Commenters connect perceived quality swings to opaque routing, load management, usage limits, and token-based billing, arguing that providers can quietly alter the product while customers cannot independently audit what they received. The recurring practical conclusion is stark: AI coding agents are useful only when they stay scoped, predictable, and cheaper than supervising their unnecessary reasoning.