Every "what's the cheapest model?" thread online is people trading vibes. I got tired of it, so I built a pipeline that pulls live, cited prices and runs the numbers through an exact-rational math kernel — no floating-point drift, no LLM hallucinating a multiplication. Then I pointed it at eight of the cost questions every agent builder actually faces. Here's everything it found, and the repo so you can re-run all of it.
There are two kinds of LLM cost advice. The first is a benchmark leaderboard with a price column, which tells you nothing about your workload. The second is a confident tweet that's quietly wrong because someone multiplied a per-million price by the wrong token count in their head.
I wanted a third kind: a model you can audit. Re-run it and you get bit-identical numbers, with the source of every input price one file away. This article is the result — a tour through eight cost decisions, each answered with real money math, and each one teaching something that intuition gets wrong.
Let's start with the question that kicked it off.
Cold open: what's the most cost-effective model to run an agent on?
