There are objective ways to compare models. They involve repeated sampling and statistical analysis to determine whether the results are likely to hold up in the future or whether they're just a fluke. If you fine-tune each model to achieve its full potential on the task you expect to be giving it, the rankings produced by different benchmarks even agree to a high degree: https://arxiv.org/abs/2507.05195

The author didn't do any of that. They ran each model once on each of 13 (so far) problems and then they chose to highlight the results for the 12th problem. That's not even p-hacking, because they didn't stop to think about p-values in the first place.

LLM quality is highly variable across runs, so running each model once tells you about as much about which one is better as flipping two coins once and having one come up heads and the other tails tells you about whether one of them is more biased than the other.

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jiggunjer 2 hours ago | parent [-]

That's objective metrics. Not an objective way to compare, which is the selection of metrics to include.

	▲	cromka 2 hours ago \| parent [-]
		That's exactly why there's a ton of different benchmarking suites used for evaluating hardware performance. I reckon we'll have similar suites comparing different aspects of models. And, at some point, we'll be dealing with models skewing results whenever they detect they're being benchmarked, like it happened before with hardware. Some say that's already happening with the pelican test.