f calculations relying on DFT energies are highly sensitive to the geometry-optimization level, this dependency largely vanishes when single-point energies are computed at the coupled-cluster levels. This insensitivity to geometry optimization level is especially advantageous for DLPNO//DFT composite methods, as it allows computationally efficient geometry optimizations without sacrificing accuracy. Indeed, this robustness extends even to semiempirical methods (Figure ), and becomes more pronounced with CCSD(T)-F12 methods, which maintain high accuracy even with lower-level optimized geometries, thereby providing an effective way of balancing computational cost and accuracy when computing ∆H • f values. What is more, the explicitly correlated F12 correction for coupled cluster methods proves crucial for achieving reliable results. Without F12 corrections, DLPNO-CCSD(T) improvements can be inconsistent, especially with smaller basis sets such as def2-SVP, which sometimes yield errors exceeding those of simpler single-step DFT methods. This underscores the importance of systematic benchmarking when designing new composite methods, as poorly chosen method combinations can inadvertently inflate computational cost while reducing accuracy. Moreover, the tested DLPNO-CCSD(T)-F12d methods consistently outperform conventional CCSD(T) in both accuracy and efficiency. Although the version of ORCA we used lacks an UCCSD(T)-F12 implementation that constrains direct comparisons for open-shell species, the closed-shell results (Table ) confirm that DLPNO variants often surpass traditional CCSD(T) methods, despite lower computational overhead. In addition, DLPNO-CCSD(T)-F12d also provides higher accuracy and better efficiency than established composite methods such as CBS-QB3 and G4.