The CASP16 Evaluation of Model Accuracy (EMA) experiment assessed the ability of predictors to estimate the accuracy of predicted models, with a particular emphasis on multimeric assemblies. Expanding on the CASP15 framework, CASP16 introduced a new evaluation mode (QMODE3) focused on selecting high-quality models from large-scale AlphaFold2-derived model pools generated by MassiveFold. Three primary evaluation tasks were therefore conducted: QMODE1 assessed global structure accuracy, QMODE2 focused on the accuracy of interface residues, and QMODE3 tested model selection performance. Predictors were evaluated using a diverse set of OpenStructure-based metrics, and a novel penalty-based ranking scheme was developed for QMODE3 to handle score interdependence and varying prediction quality distributions. Additionally, we explored the accuracy and utility of predicted local confidence measuresnow made available on a per-atom basis by methods that invoke AlphaFold3. Results showed that methods incorporating AlphaFold3-derived features – particularly per-atom pLDDT – performed best in estimating local accuracy and in utility for experimental structure solution. For QMODE3, performance varied significantly across monomeric, homomeric, and heteromeric target categories, and underscored the ongoing challenge of evaluating complex assemblies.