Crystal plasticity simulations remain computationally prohibitive for generating the large-scale datasets required by machine learning-driven materials design. Although 3D crystal plasticity simulations at a useful resolution have become feasible, executing hundreds to thousands of simulations for materials design (with or without AI) remains intractable with full-scale models. Clustering-based reduced order methods, such as Self-Consistent Clustering Analysis (SCA), have been proposed to accelerate nonlinear simulations by reducing the number of degrees of freedom of the system. This work systematically evaluates the accuracy versus computational cost trade-off for a clustering-based formulation. The study focuses on three metrics essential for predictive accuracy: macroscopic yield surface fidelity; texture evolution during monotonic, cyclic, and nonproportional loading; and local stress/strain heterogeneity. By exploiting the reusability of cluster interaction matrices across parametric studies, clustering methods enable rapid dataset generation. Additionally, adaptive strategies are explored to dynamically refine cluster granularity based on loading-path complexity, ensuring accuracy in critical regions such as grain boundaries. We observe that these metrics exhibit differential sensitivity to the degree of order reduction: while large clusters suffice for determining the yield surface, the prediction of texture development and strain localization requires finer resolution. This research provides guidelines for deploying clustering methods in scenarios requiring repeated simulations, such as microstructure design and training data generation for machine learning models.