One problem that QSPRpred is trying to address is how to bring models built with different algorithms and, thus, different software requirements under one roof and how to run and benchmark them with as similar API as possible. Therefore, in this case study we show an example that integrates and compares both the XGBoostRegressor model and a deep learning based method Chemprop in one benchmarking experiment on several data sets. Both models have different hardware and software requirements with XGBRegressor being CPU-based and accepting fingerprints as input and Chemprop requiring GPU-accelerated training and raw SMILES as input. With the exception of raw standardized SMILES for Chemprop, the same fingerprint, replica counts, splitting strategies and evaluation metrics were used in this case as well. The MedianModel was again used as a simple baseline.