evaluated two models on the same classification job. one labeled karaoke nights as music events

Reddit r/AI_Agents 08/17/26, 10:44 PM News

ai-models evaluation classification confidence-scores gpt-4o production-deployment real-world-data

Summary

Evaluation of gpt-4o-mini and gpt-4o on an event classification system showed gpt-4o performed better, but both models had unreliable confidence scores for real-world decision-making.

built an event classification system for a discovery platform. every event gets scored on 14 dimensions, genre, subgenres, energy, emotional tone, texture, the works. i ran a proper evaluation on 251 real events before trusting either model in production. gpt-4o-mini failed in ways that would have quietly killed the product. 52.5% of events got dumped into eclectic / open format , the model's version of a shrug. genre filtering is useless when half your catalog shares one label. it also had 0% non-music filtering, so trivia nights, karaoke and bowling all got classified as music events instead of being thrown out. half the events came back with empty subgenre fields. gpt-4o on the same job: 19.1% eclectic, 100% of non-music filtered, 98% subgenres populated. production ready but here's the finding that applies to every agent build. both models pinned their confidence score at 0.90 on over 90% of events. the confidence field was decorative. you can't use it to route uncertain cases to human review, which is the entire reason you ask for a confidence score in the first place. the lesson i keep relearning "it looks right in testing" is not an evaluation. count the failures on real data before launch and treat self-reported confidence as untrustworthy until you've checked its distribution does anyone have a confidence calibration approach that actually works or is everyone deriving uncertainty from something other than the model's own number?

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