Reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the combination of physically based numerical ice sheet models matched with geologic records of ice sheet mass loss. Cosmogenic nuclide measurements record the progressive exposure of mountain peaks, thus providing key geologic information to constrain the history of ice sheet deglaciation. These exposure-age datasets are spatially limited, but we can use ‘best-fit’ model simulations to extrapolate these mountain-side records to reconstruct regional deglacial behavior. Selecting a ‘best-fit’ model simulation, however, necessitates a robust model-data scoring methodology. This tool extracts modeled ice thinning histories at any given site around Antarctica, spanning the last deglaciation (20 ka to present). If the specified site contains cosmogenic nuclide exposure-age measurements of ice surface deflation, the tool plots and calculates a model-data misfit score for each selected model. A detailed account of the model-data scoring methodology can be found in [ref: Halberstadt et al, submitted], where we describe the development and use of a model-data evaluation framework using terrestrial exposure age data. In that paper, we compute site-by-site model-data misfit scores (as plotted here) and then we combine site scores across the continent to assess and interpret Antarctic-wide model scores. We also describe the implementation of a nested model framework in which high-resolution domains (2 km resolution) are downscaled from a continent-wide ice sheet model. This tool provides hands-on, interactive access for evaluating model performance at specific sites around Antarctica, especially where exposure-age data have been collected.