Academically a - very - interesting article, which confirms my ongoing suspicion of LLM Benchmarks in general ( probably why I like it so much). Another thing that caught my eye is DeepSWE having a bunch of bugs in them. The more I learn about these benchmarks the more sceptical I become. I would expect such a prolific benchmark to have these bugs ironed out by now.

The practical takeaway, however, seems to be an interesting "don't use max mode". The passage about deepseeks weird API behavior dropping to pretty much zero the moment you used high says a lot.

Also, personally, even though Opus is clearly so much ahead of the Open Source models after the changes, I think the adjusted score is still very much competitive. I see other commenters using this as proof to justify why their opinions of the open source models - but a drop from 74% to ~50% is, while a big drop, still about a ~50% pass rate afterwards. That's far from useless for coding, especially since DeepSWE seems to be such a difficult benchmark. It simply seems to more accurately reflect the capabilities of these models.

All in all, I'd love to see more of such articles, and will probably keep an eye on this one - if they continue "benchmarking" these models this way.