Information on higher-rung approximations, such as doublehybrids, is limited in this assessment due to their exorbitant computational overhead and infrequent implementation in MD software packages . The substantial cost of hybrid functionals also poses a significant challenge for obtaining extensive results in MD simulations , in particular in the context of plane wave based approaches. To tackle this issue, the emergence of machine learning (ML)-based interaction models has shown the potential to attain a similar level of accuracy at a fraction of the cost . Nevertheless, the effectiveness of such ML potentials primarily depends on their reliability across the entire phase (configurational) space sampled during MD (MC) simulations Hereafter, we present structural properties (in terms of radial distribution functions, coordination numbers, density, number of H-bonds and angular distributions) and dynamical characteristics (quantified via diffusion coefficients and rotational correlation times) obtained with AIMD and all the later generation Minnesota functionals. Those include some of the most employed meta-GGAs and hybrid meta-GGAs in computational chemistry . Meta-GGAs are investigated with Car-Parrinello MD (CPMD), while the much more computationally expensive hybrid meta-GGAs have been run with Born-Oppenheimer MD (BOMD), thanks to the crucial acceleration of a recent ML-aided multiple time step scheme that preserves the target DFT level description by construction . Both CPMD and BOMD employ classical propagation of nuclei; however, capturing a comprehensive picture of water including nuclear quantum effects (NQEs) requires more sophisticated and considerably costlier (approximately two orders of magnitude 114 ) ab initio path integral MD (PIMD) approaches . Alternatively, NQEs can be qualitatively evaluated based on very recent studies that employ DFT/ML-based PIMD methods . This allows an identification of the most promising XC functionals worth further investigation in conjunction with quantum nuclei.