HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset of HOMO/LUMO energies for QM9 compounds, calculated using the GW method. The GW method offers adequate HOMO/LUMO prediction accuracy for diverse applications, exhibiting mean unsigned errors of 100 meV in the GW100 benchmark dataset. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We anticipate that this dataset will enable the development of more accurate machine learning models for predicting molecular properties.