I find it's often very language and sector dependent. I still see a massive difference in systems programming (normally c++ and rust) between any open model I've tried and something like sonnet 4.5 (not really tried 4.6). And honestly, even the big models (like Opus 4.6) struggle in many cases.

Perhaps these things aren't well represented in the training data for these open models? Every local model I've tried (minimax2.5, GLM-4.7, Quen3, 3.5 and -coder variants) spend so much time trying to get something syntactically sensible and accepted by the compiler that when they've finished they barely seem to have any "momentum" left to actually solve the problems, as pretty much anything but the most trivial change ends up in another loop of actually trying to get it working again, often losing the intent of that change in the process.

My fear is that the solution here, having multiple instances all making the same changes for later comparison, would spend a huge amount of time beating it's head against compiler errors, types, memory allocation (NO DON'T JUST SPRINKLE IN A FEW MORE RAW "new" KEYWORDS DAMMIT) before it even gets to the "logic".

Having plenty of local GPU power I'd love to be able to actually use that, and I'm already wary about some of the training data use and it's interactions with the license of the code I'm "sending" to the cloud models...