Composite methods, often referred to as "QM recipes," aim to achieve higher accuracy at lower computational cost by intelligently combining multiple QM calculation steps. To more thoroughly evaluate the utility of such composite approaches for computing ∆H • f , we selected five promising DFT levels for geometry optimizations based on preliminary benchmarks and paired them with various DLPNO single-point energy calculations to identify the most effective combinations. Specifically, we chose B97-1/6-31G(D) as a cost-efficient baseline, B3LYP/CBSB7 for direct comparison with the classical CBS-QB3 composite approach, and three widely used hybrid functionals ωB97X-D/def2-SVPD, ωB97X-D/def2-TZVPD, and M06-2X/def2-SVPD that performed well in standalone tests. Collectively, these five methods span different accuracy-cost trade-offs. The performance of each QM recipe is summarized in Table . The results reveal several key insights about optimizing DLPNO//DFT composite method performance, including the roles of geometry optimization level, explicit correlation corrections for coupled-cluster calculations, basis set selections, and numerical parameters. § Tight (T) or normal (N) settings for self-consistent field (SCF) and pair-natural orbital (PNO), where applicable.