Numerical embeddings map scalar features into vectors before a click-through rate (CTR) backbone combines the fields. To our knowledge, NumEmbBench is the first open benchmark dedicated to comparing numerical embeddings for CTR prediction. We classify 18 configurations into six mechanism-based families and evaluate them through one FuxiCTR interface. After equal-budget validation tuning, we compare frozen configurations on three datasets, nine backbones, and five seeds under a shared train-only z-score protocol. DEER has the best mean rank, but six methods lead at least one dataset--backbone setting. A matched native-scale test changes the winner in 11 of 27 settings, while paired latency measurements show that accuracy and inference cost need not move together. Code and per-seed results: https://github.com/YH-learning/NumEmbBench

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