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The increasing use of high-throughput computational chemistry demands rigorous methods for evaluating algorithm performance. We present a Bayesian hierarchical modeling paradigm (brms/Stan) for analyzing key performance metrics: function evaluations, computation time, and success/failure. This framework accounts for variability across different systems and functionals, providing reliable uncertainty estimates beyond subjective visual assessments or frequentist limitations. We applied this to ...| Materials Cloud Archive