The best upgrade for your AI pipeline is measurement over intuition
Model choice usually comes down to a hunch. A model has a good reputation, or a high price, or it topped a leaderboard somewhere, so it gets wired into the pipeline and the decision is never revisited. The reasoning feels safe: the respected model is probably the right one, and the expensive one is probably better than the cheap one.
That reasoning is testable.
Six models were scored on three tasks of rising difficulty, from a trivial extraction to an expert-level concurrency audit, each answer graded against the same criteria. The results contradict all three hunches, and they point to a cheaper, more reliable way to choose.