Zero-inflated models and hurdle models are both designed to handle data with an excess of zeros or other specific values. The main difference between the two lies in their approach to modeling the data. Zero-inflated models assume that the data is generated from two different processes: one that generates only zeros and another that generates both zeros and non-zero values. In contrast, hurdle models separate the data analysis into two stages: the first stage focuses on the presence or absence of the specific value (e.g., zero), and the second stage analyzes the non-zero or non-specific values using a different distribution or modeling approach.