MOx resists have matured into promising alternatives to conventional CAR resists for advanced-node EUV lithography where these materials offer potential improvements to patterning fidelity and high etch resistance based on metallic components. This is a particular boon for processes with limited exposure latitudes such as High-NA EUV lithography. Creating and employing first-principle models of MOx lithographic processes should speed adoption and development of these materials and represents an important aspect of platform maturation. Stochastic photochemical models of metalcontaining resist systems have previously been developed, but without extension to computational lithography. Likewise, stochastic models derived from CAR systems have been fit to MOx lithographic data using computational lithography software, facilitating limited stochastic lithography studies without capturing fundamental MOx imaging processes. Recently, a rigorous stochastic model built from the ground up using MOx-specific resist principles has been developed. In this contribution, the performance of this MOx-specific model was assessed by comparing simulated and experimental lithography data for a series of MOx resists under a range of exposure and process conditions. Chemical and physical properties of the resists derived independently from X-ray diffraction, EUV absorbance, FTIR spectroscopy, and ellipsometry measurements were parameterized in the context of the simulation, and calibration routines were used to fit simulated data to experimental CD-SEM exposure data produced using an NXE-3300B EUV scanner. Insights from these models may be used to guide MOx resist development and EUV lithography process optimization. Ultimately, these studies will help to identify process windows, processing points, and possibly improvements to the MOx resists.