The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms, which involves an analysis of the stiffness performance of numerous discrete points in the workspace. This necessitates time-consuming and inefficient calculation, which is particularly pronounced in the optimization design stage of the mechanism, where the variations in the global stiffness performance indices versus various dimensional and structural parameters need to be analyzed. This paper presents a semi-analytical approach for stiffness modeling of the novel (R(RPS&RP))&2-UPS parallel mechanism (referred to as the Trifree mechanism) and proposes “local” stiffness performance indices as alternatives to global indices. Drawing on the screw theory, the Cartesian stiffness matrix of the Trifree mechanism is formulated explicitly by considering the compliances of all elastic elements and the over-constraint characteristics inherent in the mechanism. Based on the spherical motion pattern of the Trifree mechanism, four special reference configurations are extracted within the workspace. This yields “local” stiffness performance indices capable of accurately evaluating the overall stiffness performance of the mechanism and effectively improving the computational efficiency. The variations in global and “local” stiffness performance indices versus key design parameters are investigated. Furthermore, the proposed indices are applied to the Tricept and Trimule mechanisms. The results demonstrate that the proposed indices exhibit excellent computational accuracy and efficiency in evaluating the overall stiffness performance of these spherical parallel mechanisms. Moreover, the stiffness performance of the novel parallel mechanism investigated in this study closely resembles that of the well-known Tricept and Trimule mechanisms. This research proposes a semi-analytic stiffness model of the Trifree mechanism and “local” stiffness performance indices to evaluate the overall stiffness performance, thereby substantially improving the computational efficiency without sacrificing accuracy.