arXiv:2610.31008v1 Announce Type: cross Abstract: Global sensitivity analysis quantifies how strongly inputs influence a model output, however, input importance often varies across the model space. We introduce a heterogeneity index H to quantify this variation. The index compares normalized regional sensitivity profiles using mean pairwise total-variation distance and its values range from zero to one. We test H on nine benchmark functions and examine its robustness to the number of regions, sample size, and sampling strategy. We further demonstrate it using models of wooden-pallet carbon footprint, flood risk, and steel-structure reliability. Across these cases, H reveals changes in sensitivity that global indices obscure, including inputs whose importance changes substantially across the model space and differences between rare behavioral regimes. The index complements global sensitivity analysis by indicating when a single global sensitivity profile is insufficient. An open-source implementation is provided in the simdec package.