We assess and compare the models and their associated latent spaces in a number of ways. We begin by performing a traditional assessment through model reconstruction accuracies on the training and testing datasets. To assess the generalizability of the models, we benchmark their generative capacities in terms of the distributions they are capable of sampling, a process which is becoming more and more imperative as generative models become more commonplace . To assess the meaningfulness of associated distance metrics, we perform a quantitative and qualitative assessment of the models' ability to cluster the data in a low-dimensional space produced by principal components analysis (PCA) and a detailed analysis of the top five principal components. To assess model interpretability and address a longstanding problem in the field, we employ metrics from manifold theory to assess the extent to which PCA distorts the latent space embedding. Leveraging PCA's enhanced interpretability we connect latent representations to peptide properties through bridge variables. Finally, we demonstrate the use of our models in a pipeline. Unless otherwise noted, all analyses are performed on the test set data.