Solar-J is a comprehensive model for radiative transfer over the solar spectrum that addresses the needs of both photochemistry and solar heating in Earth system models. Solar-J includes an 8-stream scattering, plane-parallel radiative transfer solver with corrections for sphericity. It uses the scattering-phase function of aerosols and clouds expanded to 8th order and thus makes no isotropic-equivalent approximations that are prevalent in most solar heating codes. It calculates both chemical photolysis rates and the absorption of sunlight and thus the heating rates throughout the Earth's atmosphere. Solar-J is a spectral extension of Fast-J, a standard in many chemical models that calculates photolysis rates in the 0.18–0.85 μm region. For solar heating, Solar-J extends its calculation out to 12 μm using correlated-k gas absorption bins in the infrared from the shortwave Rapid Radiative Transfer Model for GCM applications (RRTMG-SW). Solar-J successfully matches RRTMG's atmospheric heating profile in a clear-sky, aerosol-free, tropical atmosphere. We compare both codes in cloudy atmospheres with a liquid-water stratus cloud and an ice-crystal cirrus cloud. For the stratus cloud both models use the same physical properties, and we find a systematic low bias in the RRTMG-SW of about 3 % in planetary albedo across all solar zenith angles, caused by RRTMG-SW's 2-stream scattering. Discrepancies with the cirrus cloud using any of RRTMG's three different parameterizations are larger, less systematic, and occur throughout the atmosphere. Effectively, Solar-J has combined the best components of RRTMG and Fast-J to build a high-fidelity module for the scattering and absorption of sunlight in the Earth's atmosphere, for which the three major components – wavelength integration, scattering, and averaging over cloud fields – all have comparably small errors. More accurate solutions come with increased computational costs, about 5x that of RRTMG, but there are options for reduced costs or computational acceleration that would bring costs down while maintaining balanced errors across components and improved fidelity.