Minifying code cuts AI agent tokens 42%

Minifying code cuts a coding agent's input tokens 42% on average, according to a paper posted to arXiv in June. For agents that read whole repositories into context, that's real savings on cost and latency without touching the model itself. It comes at a price: accuracy on SWE-bench Verified drops 12 points when the code gets stripped down first.

The paper's case is that source code, not conversation history or tool output, is the dominant token sink for agents that keep full repo state in context. Minification strips non-essential lexical elements, things like whitespace and dead syntax, while preserving semantics, so the agent still sees working code, just less of it. The code behind the paper is public on GitHub.

I'd skip minifying the whole repo. The smarter move strips only the files an agent reads for context and leaves the ones it's actually editing untouched, since that's probably where the 12-point accuracy hit lands hardest. Worth watching whether anyone publishes numbers on selective minification instead of the blanket version.

The paper posted to arXiv in June and its code is already public, so teams running agents over large repos can test the tradeoff themselves this week.