@nutlope had Jev play chess against GLM 5.3. GLM won by checkmate in 29 moves. They report about 0.3s and under $0.0001 per Jev move, about 5.8s and $0.008 per GLM move, and 24 cents for the game. The takeaway they state is to use Jev for fast classification and an LLM when reasoning or lookahead is needed.

Hassan (@nutlope) published this on 2026-09-18. @nutlope had Jev play chess against GLM 5.3. GLM won by checkmate in 29 moves. They report about 0.3s and under $0.0001 per Jev move, about 5.8s and $0.008 per GLM move, and 24 cents for the game. The takeaway they state is to use Jev for fast classification and an LLM when reasoning or lookahead is needed. Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole game cost 24 cents My main takeaway is that it's often useful to use each one to their strengths: ◾ Fast, well-defined classification → specialized models like Jev ◾ Classifications that need reasoning or lookahead → LLMs like GLM 5.3 ◾ Real classification pipelines → hybrid. How Jev or TypeSafe is used, in the post's own words: “Jev vs GLM 5.3 at chess!” The post does not name a separate audience. It is for people reading what @nutlope shows here (it includes media).