Why it matters
Tokenmaxxing shows up when teams default to frontier models for every step in an agent loop. Routing can cut spend, but only if it avoids prompt-cache thrash and keeps quality predictable across “easy” and “hard” turns.
Tokenmaxxing read
Treat routing like a token budget scheduler: keep the strongest model for the reasoning-heavy turns, but route setup/tests/tool-followups to cheaper options. The key constraint is caching — if switching evicts the prompt cache too often, the “savings” disappear.
Source takeaway
Augment claims the top ~10% of turns consume a majority of LLM rounds inside IDE agent loops, and that cache-aware, sticky routing can deliver ~20–30% lower cost while staying close to target frontier-model quality on their internal multi-turn benchmark.
