Does anyone else feel like AI benchmarks are becoming less useful for predicting real-world performance?

A lot of recent models are scoring incredibly well on benchmarks, but actual day-to-day usage often feels very different from leaderboard expectations. In practice, teams seem to care more about things like: * consistency over long sessions * latency * context handling * tool use reliability * cost efficiency * how well models recover from mistakes * developer workflow quality Some models feel amazing in demos/evals but become frustrating during sustained real-world usage because they: * over-explain * lose focus over long contexts * become repetitive * struggle with orchestration-heavy tasks Feels like we might be entering a phase where infrastructure + workflow quality matter almost as much as raw model intelligence. Curious if others are seeing the same thing or if benchmarks are still matching your real-world experience closely.