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Ranking AI coding agents by cost per successful task can reward a system for giving up cheaply, argues the author of a new Stet.sh essay. That metric may also miss harder-to-quantify differences, such as whether the resulting code is maintainable.
Why it mattersThe author favours head-to-head comparisons, relative win rates and evaluations split by the shape and constraints of the work. It is a useful reminder that a low bill is not much of a bargain if the agent fails the awkward tasks or leaves a mess behind. How should coding agents be judged: by cost per success, or by comparisons that account for what they can actually do?
Discuss: Should coding-agent evaluations prioritise cost per successful task, or measure performance and code quality across different kinds of work?
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