A benchmark score can age faster than the model it ranks
A single leaderboard snapshot can hide a model getting worse at the work you actually care about. In a study spanning eight models, four tasks, and eight languages, Gemini 2.5 Pro led overall at 4.73/5, yet no model won everywhere, and GPT-5’s strengths in translation did not carry over to content generation in Chinese and Polish. The practical fix is repeated, task-specific evaluation, not blind trust in release-era scores.