If you can run your tests fast and cheaply, and have metrics that show what bad/sloppy code is that are cheap & fast to generate, a worse fast model can outperform a far better far slower model if you value time...

I've had pretty good success with LLMs after putting in place metrics to measure true complexity (not cyclomatic), and automatically pushing back everything until the added complexity is within reason for the feature.