Every leaderboard row is somebody saying their setup produced this number. The claim is cheap to make and expensive to check, which is why most write-ups skip the checking part.
Published scores rarely ship with the conditions attached: prompt version, harness code, sampling settings, how many runs the number averages.
A price with no line items tells you nothing about what you are buying. Scores work the same way. Context length, temperature, prompt set, harness: each one moves the number, and each one is left off the box.
Two models a few points apart may be measuring different things. That difference never shows up in the headline.
Ask any score for four things: the harness, the prompts, the serving config, and the variance across runs.
A write-up that answers all four did the work. One that answers none is handing you an ad with a decimal in it.
PastAGI publishes audits that keep the line items attached: pricing worked down to dollars per serving hour, serving math shown beside the claims, coverage of what stays in a reasoning trace after summarization.
Start with PastAGI, AI guides that audit the benchmarks, or the audit-first AI engineering publication behind the serving-math reviews.