In Part 1 of this series, we evaluated two widely used correction schemes for improving QM-based ∆H • f : bond-additivity corrections (BACs) in the styles of Petersson and Melius, and isodesmic reaction (IDR) schemes. We implemented both in the opensource software Arkane, which integrates with major quantum chemistry packages and automates thermochemical and kinetics analyses. Our benchmarks on 15 frequently used model chemistries (a.k.a. "QM methods" or "levels of theory") showed that both Peterssonand Melius-type BACs yield substantial reductions in errors relative to uncorrected QM results and generally outperform isodesmic reactions in accuracy and robustness. Building on that foundation, in this Part 2 we systematically benchmark 284 model chemistries for ∆H • f calculations, aiming to delineate practical guidelines for choosing costeffective yet accurate methods, particularly for small organic molecules. These model chemistries span semiempirical methods, density functional theory (DFT), wave function theory (WFT), and composite schemes. Particular attention is paid to basis set selection, geometry optimization levels, and explicit correlation treatments (F12) in coupled-cluster methods, as these choices critically affect both accuracy and computational expense. We investigate how well these methods perform for different categories of molecules (e.g., neutral singlets, ions, radicals, zwitterions), and we further validate top-performing approaches on an independent test set of 500 species. This comprehensive analysis aims to provide actionable recommendations for researchers who need accurate ∆H • f results over diverse molecular contexts, including those encountered in de novo discovery, high-throughput screening, and machine learning applications. Ultimately, our study offers a pathway to generating large-scale, multi-fidelity thermochemistry datasets that can accelerate datadriven innovation in chemistry and materials science.