You can get far more gas out of even a $20 plan of you’re careful to break things up into relatively small discrete steps, clear context regularly and give the model plenty of information to work with.
My workflow for bigger features is to write out a plan document and then proceed in smaller implementation steps, reviewing as I go. If I find something odd, I ask the agent why and often that leads to discovering a new dimension to the problem, which in turn is an opportunity to adjust the approach.
Only if money is no object. Cache reads are cheap (10% of uncached input costs) but definitely not free, and cached reads dominate session costs at long context lengths. A prompt at 20k context with $0.01 in cached reads would cost $0.40 in cached reads at 800k context, that quickly adds up for long sessions.
Personally I find using /rewind judiciously is better than using /compact. The latter essentially gives you no control of what details to discard, but the former at least has coarse-grained control.
My whole point was that by planning in advance you can shard the work into manageable sections with clear beginnings and outputs with acceptance croteria, and never compact, or even use more than a few hundfed thousand tokens in context.
It’s all hierarchical. Looking at an eval feature building right now, it’s 20ish build plans, each with zero to five or so /clear moments.
But maybe that’s the key thing… I don’t iteratively prompt ad hoc software writing. I do iterate on requirements, but if those are solid enough there is no “now write this function, now write that module”.