Inverse thermal history modeling is an effective tool to explore plausible time-temperature (t-T) histories that can be used to describe the geologic history of a sample. Although in some inverse modeling exercises the input thermochronology data is consistent with a single set of t-T histories with similar heating and cooling trends, more commonly inverse models identify a range of paths with different and distinct heating and cooling histories but similarly good fits to the data. Each set of these “path families” typically requires a different geologic interpretation to explain the observed heating and cooling trend, so it is important to identify and consider all possible path families consistent with the regional geology that fit the modelled dataset before selecting a preferred geologic interpretation. Although the inverse model results are always consistent with measured data, a model’s ability to detect all possible path families is partly controlled by the model design – for example the choice of initial conditions, monotonicity settings, and forced time-temperature windows. In this exercise using the thermal history modeling program HeFTy, we illustrate the effects of model design on the inverse model results of a set of multi-chronometer datasets from the southern Patagonian Andes. We use model design to maximize the number of path families identified through inverse modeling. Once individual paths are classified according to path families, we use independently constrained regional geology to discriminate among the diverse plausible set of path families and evaluate different available geologic scenarios. Our exercise illustrates that models restricting exploration of all path families may not identify the true cooling history of the sample. Initially, it may appear challenging to interpret inverse model results that include multiple path families, but we argue that iterating between independent geologic data and modeling provides an effective tool to test the geologic plausibility of alternative heating and cooling histories. Although this exercise is executed using HeFTy, maximizing the identification of all possible path families should be an important component of model design in inverse modeling exercises using all inverse modeling programs.