We Tested TypeSafe AI's Jev on Screen Classification: 20x Faster, 153x Cheaper, and Noticeably Less Accurate

Fair point on tokenisation hiding typos, though it does not quite apply to Jev itself since it is not generating text token by token; it returns a fixed schema field per call. Where this could actually bite us is one step earlier: we generate the screen description with a separate LLM (Bonsai) before Jev ever sees it, so tokenisation weirdness there could bleed into Jev's classification. We did not isolate for that in this experiment; it's worth checking against the "others" misses specifically. Do you have a specific case in mind, or a tool you use for that raw character vs token view comparison?