I'm doing a lot of rearchitecting/refactoring and hardening in a large handwritten codebase; iterative performance and storage optimization (some areas I've been iterating on since January, with tremendous new progress unlocked by each new model release); offline-friendly, multi-device realtime sync with complicated requirements; and various natural language processing and other such problems that are essentially unsolvable but can become more accurate and better tested for accuracy through iterative work. Off the top of my head.
Some of these tasks involve a lot of code reading or other inputs, or reevaluating work. Token efficiency is no help there, if the input can't simply be skipped. Cheaper models are sometimes bad at summarizing or highlighting the right parts, depending on the task.
I also don't use subagents except for Luna. I'm mindful of cached sessions and start new ones often to avoid loading in full contexts (often with some kind of handoff doc or skill).