Predictive models are used to estimate exposures from consumer products to support risk management decision-making. These model predictions may be used alone in the absence of measured data or integrated with available exposure data. When different models are used, the resulting estimates of exposure and conclusions of risk may be disparate and the origin of these differences may not be obvious. This Perspectives Paper provides recommendations that could promote more systematic evaluation and a wider range of applicability of consumer product exposure models and their predictions, improve confidence in model predictions, and result in more accurate communication of consumer exposure model estimates. Key insights for the exposure science community to consider include: consistency in product descriptions, exposure routes, and scenarios; consistent and explicit definitions of exposure metrics; situation-dependent benefits from using one or multiple models; distinguishing between model algorithms and exposure factors; and corroboration of model predictions with measured data.