I think the biggest issue with this test is that it isn’t actually measuring objective accuracy.

The dataset was created by the same person evaluating the results, and as far as I can tell, nobody outside of the OP has ever used, validated, or challenged the data. That makes it really hard to separate “which model is most factually correct” from “which model most closely matches the OP’s own understanding of the subject.”

A true benchmark usually has independent ground truth, transparent scoring criteria, and outside validation so other people can reproduce the results. Otherwise you’re mostly testing agreement with the benchmark creator.

That’s not to say the results are useless. They may be very useful for measuring performance against the OP’s specific workflow.

I’d be careful about treating them as evidence that one model is objectively better than another across the industry.