I'm not sure people are really trying to interpret this kind of benchmark as being accurate in gauging the magnitude of improvement. It seems pretty obvious that doubling your score on some benchmark where 100% means "correctly answered all of these specific problems" doesn't translate directly to performing twice as well on all problems. I think what people want from these benchmarks—and what they do get to some extent—is answering the question of "is model A better than model B", especially the subset of "is this local model better than last year's frontier online model".

The marketing departments touting each model do want to claim superiority on the basis of slivers of percentage points, and that's probably always a stronger claim than the test results can reasonably support. And the benchmarks are obviously susceptible to cheating and overfitting. But when the scores aren't saturated and do show a big discrepancy, that kind of result usually seems to align with what people report from actually trying to use the models in the relevant problem space.