In this paper, we present a comparative benchmark of two implementations of CMA-ES, both with and without diagonal decoding. The benchmarked variants of CMA-ES with diagonal decoding adaptively split the update of the covariance matrix into an update with the original CMA-ES method and an update with the separable-CMA-ES method. Thus, the diagonal decoding should allow for improved performance on separable functions with minimal loss on nonseparable ones. To gain insight into how diagonal decoding impacts CMA-ES runs, an assessment of the performance gain or loss due to the use of diagonal decoding relative to the original CMA-ES, was performed on bbob problems using the COCO platform. We were also interested in variances that might emerge from the difference in the implementations. The data presented in this paper shows improved performance of the CMA-ES on separable functions when using diagonal decoding, without any apparent loss on nonseparable ones. In addition, a few performance variances were spotted in the weakly structured functions, which appeared uncorrelated with the use of diagonal decoding. However, they can be traced back to implementation differences, such as the stopping conditions that may result in different runs, as the data suggests.