In this work, we benchmark GPT-3 on datasets spanning chemical space from molecules over materials to reactions (see Supplementary Note 2). On those datasets, we investigate different tasks ranging from classification, i.e., predicting a class (e.g., "high", "low") given a text representation of a molecule, material, or reaction, regression, i.e., prediction of floating point numbers, to inverse design-the prediction of molecules. MOF rendering created with iRASPA.