Observations can be considered as one realization of the climate system that we live in. To conduct meaningful evaluation of climate models one must use multiple ensemble members from a single model and assess where the observations sit within the ensemble spread. Single model initial-condition large ensembles (LEs) are valuable tools for such an evaluation. Here, we present the new multi-model large ensemble archive (MMLEAv2) which has been extended to include 16 models and 11 key climate variables. Data in this archive have been remapped to a common 2.5 x 2.5 degree grid to promote inter-model comparison. We also introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with LEs. By applying this package to the MMLEAv2 we highlight its value in model evaluation and inter-ensemble comparisons. We demonstrate that for some metrics depending on which ensemble member is chosen, a model might evaluate poorly or favorably due to a large ensemble spread, and emphasize that for highly variable metrics, LEs allow for more meaningful evaluation than individual ensemble members, the ensemble mean, or a multi-model mean. Post-processed output from the CVDPv6 across CMIP archives is also made available to expedite broader community analysis and understanding of model capabilities across recent model generations.