CASE FILE / Games & simulation
Jev vs GLM 5
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. Jev handles the easy calls, an open model handles the hard ones. The future is multi-model!
Original post
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. Jev handles the easy calls, an open model handles the hard ones. The future is multi-model!
Chinese translation
国际象棋Jev vs GLM 5.3！ 结果： GLM 5.3被将军29步拿下 Jev：每次动作~ 0.3秒且<0.0001美元 GLM 5.3：每次动作~ 5.8秒，约0.008美元 整个游戏花费24美分 我的主要收获是，充分利用每个人的优势通常是有用的： 快速、定义明确的分类-Jev等专业车型 需要推理或前瞻的分类-GLM 5.3等LLM 真实分类管道-混合。Jev处理简单的电话，开放模式处理困难的电话。 未来是多模式的！
What this case shows
- Game state maps cleanly onto typed choices
- Let JEV pick; keep generation elsewhere
- The original post usually has a playable demo