There are many ways to skin a cat.

Often all it takes is to reset to a checkpoint or undo and adjust the prompt a bit with additional context and even dumber models can get things right.

I've used grok code fast plenty this week alongside gpt 5 when I need to pull out the big guns and it's refreshing using a fast model for smaller changes or for tasks that are tedious but repetitive during things like refactoring.

Yes fast/dumb models are useful! But that's not what OP said - they said they can be as useful as the large models by iterating them.

Do you use them successfully in cases where you just had to re-run them 5 times to get a good answer, and was that a better experience than going straight to GPT 5?