The model debate gets clearer when “best” is replaced with “least review cost”

A softer framing for Claude/GPT comparisons: review cost, not winner labels.

The most useful question I have seen is not “which model is smarter?” It is: after the model answers, how much human review is still needed before the work can ship, cite, or hand off? That makes the comparison less tribal. A model can be excellent at first drafts and expensive at final verification. Another can feel plain but create output that is easier to check. For real workflows, the second one often wins quietly.