Most discussions about AI coding tools start with "which model is best?" I've found that's the wrong first question. The better question is: which of my tasks actually need the strongest model, and which ones don't?
In my earlier posts I wrote about building a small evaluation suite for AI coding models and a falsification loop for reviewing AI-generated refactors. This post is the missing piece between them: a routing layer that decides, per task, whether a free-tier model is good enough — and a way to measure whether that decision was right, instead of trusting vibes.
The problem: paying frontier prices for boilerplate work
When every prompt goes to the most expensive model by default, two things happen:
You burn budget on tasks a weaker model handles fine (renaming, boilerplate, docstrings, simple test generation).
