I'm not sure about the answer here, but this can be caused by the scoring rubric used by given benchmarks. For instance, if a benchmark docks scores for running too many commands or using too much wall-clock time, higher efforts will get lower scores.

Pure speculation, but I've noticed drawbacks to the models on high effort. I interact mostly through prompts rather than agents so I sometimes see where their reasoning falls short. A model on high effort has longer output and can get hyperfocused on irrelevant details, maybe increasing the surface area for mistakes. I haven't used other effort levels extensively yet but I've supposed that medium may have more balance.

It's "Cost per task", so perhaps it burns tokens too quickly, trying to "do a better job". Over-engineering :)

Last week it felt like Opus 4.8 was moving the Pro "usage" meter very quickly. Today, pre-announcement, Opus 4.8 Medium felt like there was less meter-use per minute. And post-announcement, Opus 5 Medium also feels more efficient, allowing more work in the 5-hour window.

Completely subjective, of course.

apparently it got docked points for editing files out of scope

Maybe it's akin to the Ballmer peak: improved performance at a specific level of relaxation

I agree, very odd they did not comment on any theories for the degradation here. Dip and then rebound at max effort is pretty interesting too. Overthinking is bad, but you can overthink so much it starts to be better again?

It is probably from randomness. The benchmark tasks nowadays are so long that you can't really afford to run a large number of samples of them per model & effort combination