Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex detector data. Point clouds, however, are likely a more natural representation for many of these data sets, particularly in calorimeters with high granularity that produce very sparse images. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and datasets. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.