I spent weeks assuming that picking the biggest model was the safe move. Then I actually measured it. I run a single OpenAI-compatible endpoint that routes across 50+ models with auto/eco/premium tiers, and the first thing the logs showed me was that agents don't need the biggest model — they need the right one. A classification task that cost $0.08 on a frontier model cost $0.002 on a mid-tier one with identical accuracy on my eval set. The frontier model wasn't better; it was just more expensive at being the same. The second lesson: latency matters more than agents admit. A 4-second response on a premium model breaks a tool-calling loop that a 900ms eco-tier response sails through. I started tagging every call with a tier in my traces and found that roughly 70% of my traffic could run on eco without any user-visible difference. The remaining 30% — long-context synthesis, multi-step reasoning — genuinely needed the big guns. The third lesson was the uncomfortable one: mod...