I have not personally reviewed the benchmarks, but recall seeing a post where a Linear SVM with bag-of-words features outperformed Jev on many simple NLP classification tasks, like the ones you cite. You don't even need embeddings!
I've used LLM's to classify things for numerous projects and it's always really hard to beat linear classifier/decision tree over simple embeddings or even the sklearn HashingVectorizer. However, you DO need to trust your validated data, ground truths for this to work - but you should have these anyway to validate a Jev or similar solution.