Modeling of metabolism at the genome-scale has proved to be an efficient method for explaining thephenotypic traits observed in living organisms. Further, it can be used as a means of predicting the effect of genetic modifications for example,development of microbial cell factories. With the increasing amount of genome sequencing data available, there exists a need to accurately and efficiently generate such genome-scale metabolic models (GEMs) of nonmodel organisms, for which data is sparse. In this study, we present an automatic reconstruction approach applied to 24 Penicillium species, which have potential for production of pharmaceutical secondary metabolites or use in the manufacturing of food products, such as cheeses. The models were based on the MetaCyc database and a previously published Penicillium GEM and gave rise to comprehensive genome-scale metabolic descriptions. The models proved that while central carbon metabolism is highly conserved, secondary metabolic pathways represent the main diversity amongspecies. The automatic reconstruction approach presented in this study can be applied to generate GEMs of other understudied organisms, and the developed GEMs are a useful resource for the study of Penicillium metabolism, for example, for the scope of developing novel cell factories.