The identification of jets arising from heavy-flavor (bottom or charm) quarks primarily relies on detector inputs from reconstructed charged particle tracks and information about secondary vertices contained within reconstructed jets. In Run 3, improved machine-learning techniques have been introduced to distinguish heavy-flavor jets from those originating from the hadronization of light-flavor (uds) quarks or gluons (g). Therefore, it is crucial to compare the distributions of data and simulations of input variables, tagging discriminants, and other pertinent kinematic observables between data and simulated events. In this proceeding, five selections to enriched different processes are presented top quark-antiquark production ($t\bar{t}$) in the dileptonic final state (enriched in b jets), in the semileptonic final state (enriched in b and c jets), W boson plus charm production (enriched in c jets), Drell-Yan production, and QCD multijet production (enriched in light-flavor jets). These selections are shown with proton-proton collision data at $\sqrt{s}=13.6$ TeV corresponding to an integrated luminosity of 61.7 $\text{fb}^{-1}$ and recorded by the CMS experiment in 2022 and 2023. These studies rely on a modern and fast framework. It has been developed and automated to produce the comparisons presented, along with its technical details.