The rapid proliferation of metaverse platforms with heterogeneous architectures, functionalities, and purposes poses a significant challenge for informed technology selection. Consequently, there is a need for structured evaluation approaches that enable comparison based on functional and non-functional attributes relevant to specific application contexts. The objective of this study was to propose a model for evaluating the quality of metaverse-type platforms based on a hybridization of the aspects defined in the ISO/IEC 25000 family of standards, a maturity model extracted from recent literature, and the Metagon metaverse characterization typology. The proposed model operationalizes 35 evaluation attributes grouped into seven categories, enabling a comprehensive assessment of metaverse platforms. Using this model, 23 metaverse platforms were evaluated through a hierarchical ranking strategy with tolerance. The results show that platforms such as Decentraland and Roblox achieve the highest levels of maturity (ML5), although open-architecture platforms demonstrated superior structural robustness in comparative tie-breakers. The results provide a taxonomy of characteristics refined and validated by experts and used in the evaluation of the analyzed platforms, resulting in a reproducible classification that enables systematic comparison across different application contexts. The discussion presents the basis for future studies focused on the evaluation of specific categories, such as educational, therapeutic, or social interaction platforms.