MSNovelist reached a top-128 accuracy of 57% and top-1 of 26% on CASMI 2016 when using the sum formula predicted by SIRIUS, which was correct in 93.8% of the cases. MS2Mol does not require a sum formula as input and reached a top-25 accuracy of 9% on CASMI 2022. MassGenie 14 reported an accuracy of 53% on a subset of 93 challenges of CASMI 2017 with small molecular weight that were also used to train their model, while Mass2SMILES 13 correctly elucidated 2/236 challenges of CASMI 2022. Spec2mol could not be evaluated on the CASMI challenges, as their model requires four input spectra, combining positive and negative ionization with high and low collision energy, to elucidate a molecular structure. MADGEN is a scaffold completion model that obtained a top-10 accuracy of 1.6% on MassSpecGym when choosing a scaffold from a list of 256 options, and 38.6% when given the true scaffold of the molecule. The improvement in performance of our model can be attributed to the larger and more diverse training dataset, the data augmentation protocol employed during training, the larger model size, and the fact that the correct sum formula is given as input. As most of these models lack public and freely usable implementations, we limit ourselves to reporting their performance as originally stated in the respective publications, and to facilitate future research in this area, we open source our implementation including training code, data, and pretrained checkpoints upon peer reviewed publication at the following link at .