There are a number of deterministic mathematical approaches available for modeling indoor air pollution concentrations. These models range in complexity from simple saturation vapor pressure models to models using computational fluid dynamics, with many in between these extremes. This range reflects the variety of ways pollutant generation, transport, and mixing are treated in the different models. In selecting which model to use, a tiered approach is useful. The tiered approach considers the goal of the modeling, the availability of model inputs, and the degree of uncertainty that is acceptable. The simpler models are easy to use and the inputs are often readily available. However, they usually have an inherently high degree of uncertainty which requires conservative assumptions that may result in overestimates of concentrations. The more complex models are more difficult to use and require more precise inputs. However, they have the potential to allow the modeler to reduce or quantify uncertainty. This ability to address uncertainty may be limited by the understanding of and the availability of the model inputs. An application example of the tiered approach using a completely mixed space model, a two-zone model, and a turbulent diffusion model illustrates the selection criteria for model use and the model performances. Ultimately, an understanding of the principles behind the models and their inherent strengths and weaknesses is required for their appropriate application to exposure assessment.