In cases where maximal accuracy is needed, we recommend DSD-PBEP86 or wB97M-V. These give worse accuracy and worse performance, and should be avoided for production use. We note that this study has not comprehensively surveyed many basis-set effects; we leave this substantial task to future work, with the observation that many commonly used basis sets may not be Paretoefficient. If faster methods than r faster-faster than every semiempirical method, and even faster than GFN-FF. This is dramatically different from the results reported by Folmsbee and Hutchison in their 2021 study: in that study, the ANI-2x ML model was similar to GFN1-xTB and GFN2-xTB both in terms of cost and accuracy. In our hands, both ANI-2x and AIMNet2 are significantly faster than xTB-based methods, and AIMNet2 is substantially more accurate, particularly in terms of RMSE. (We note that the present study did not employ hardware acceleration, and that the ML methods used here are likely to be even faster when GPUs are used.)