> I don't know about good, I use a lot of local models and they're still pretty painful to run locally

You are somehow assuming cloud-based models are not painful.

I can tell you my past experience. I was using GPT 5.5 and Claude Opus interchangeably and I prompted them to implement a feature. I paid attention to the agent window and it was literally screwing up implementations, causing tests to fail, and going into test-fail-fix loops to clean up after itself. After a few minutes, it finally called it done. That run cost $0.60.

I went to review the code and only half of the source files complied with the instruction files. I prompted the model to clarify why it failed to comply with the instruction file. The model outputs "you are right, I should have complied with the instruction files. That prompt cost $0.30.

I prompted the model to proceed and apply the instruction file prompts. It went ahead and applied changes. Success. It cost $0.16.

I reviewed the code again. Only half of the sloppy code was touched up. I prompted it to fix the whole mess, not just a couple of files. It complied. One coin less in my purse.

So, around a third of the cost of a feature is spent on the model cleaning the mess it left in it's wake.

And this was a tiny feature with a plan, a solid set of instruction files.

Very expensive.

Are costs going down? I doubt so. OpenAI seems to still be spending 3 times it's revenue already.

In comparison, local models sound very good.