With an aim of providing large datasets with energy targets from wide variety of methods to help build both these novel molecular ML tasks and ML for reaction networks, we provide multi-level energies of the QM9 molecules and reactions derived from there. Molecular Quantum chemistry community utilizes a wide variety of exchange correlation functional and thus any new ML method need to be tested for predictions across many different XC to prove generalization and universality. Therefore, by providing data for QM9 molecules with a number of DFT functionals and basis set will provide new challenges to ML community to build robust ML methods that can be applied to a variety of methods. Furthermore, we provide the energies with the GFN-XTB method for the QM9 molecules and the reactions keeping in mind that reaction network exploration methods often use this semi-empirical method.