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. submitted by /u/qubridInc
Originally posted by u/qubridInc on r/ArtificialInteligence