We're at the point where copilot is irrelevant. Your way of working is irrelevant. Because that's not how you interact with coding AIs anymore, you're chatting with them about the code outside the IDE.
Just this month I've burned through 80% of my Copilot quota of Claude Opus 4.6 in a couple of days to get it to help me with a silly hobby project: https://github.com/ncruces/dbldbl
It did help. The project had been sitting for 3 years without trig and hyperbolic trig, and in a couple days of spare time I'm adding it. Some of it through rubber ducking chat and/or algorithmic papers review (give me formulas, I'll do it), some through agent mode (give me code).
But if you review the PR written in agent mode, the model still lies to my face, in trivial but hard to verify ways. Like adding tests that say cosh(1) is this number at that OEIS link, and both the number and the OEIS link are wrong, but obviously tests pass because it's a lie.
I'm not trying to bash the tech. I use it at work in limited but helpful ways, and use hobby stuff like this as a testbed precisely to try to figure out what they're good at in a low stakes setting.
But you trust the plausibly looking output of these things at your own peril.
If you check the docs, smaller, faster, older models are recommended for 'lightweight' coding. There's several reasons for this. 1) a smaller model doesn't have as good deep reasoning, so it works okay for a simple ask. 2) small context, small task, small model can produce better results than big context, big task, big model. The lost-in-the-middle problem is still unsolved, leading to mistakes that get worse with big context, and longer runs exacerbate issues. So small context/task that ends and starts a new loop (with planning & learning) ends up working really well and quickly.
There's a difference between tasks and problem-solving, though. For difficult problems, you want a frontier reasoning model.
Yes.
> It's hard to communicate the difference the last 6 months has seen.
No, it isn't. The hypebeast discovered Claude code, but hasn't yet realized that the "let the model burn tokens with access to a shell" part is the key innovation, not the model itself.
I can (and do) use GH Copilot's "agent" mode with older generation models, and it's fine. There's no step function of improvement from one model to another, though there are always specific situations where one outperforms. My current go-to model for "sit and spin" mode is actually Grok, and I will splurge for tokens when that doesn't work. Tools and skills and blahblahblah are nice to have (and in fact, part of GH Copilot now), but not at all core to the process.