Token cost per task varies sharply by language, and agents secretly prototype in Python to escape unfamiliar ones.

arXiv 2607.22807 controlled for problem difficulty across Python, Java, Rust and OCaml and found stark, model-consistent variation in tokens burned per task. The behavioral explanations are the useful part: agents produce non-compiling solutions more often in unfamiliar languages then over-revise already-passing code, use comments as a planning scratchpad, distrust supplied test cases and invent their own inputs, and sidestep unfamiliar target languages by prototyping in Python first. Budget agent spend per language, and supply trusted test harnesses explicitly, because the agent will otherwise ignore the one you gave it. (arXiv 2607.22807)