The 16th Critical Assessment of Structure Prediction benchmarked advancements in biomolecular modeling, particularly in the context of AlphaFold 2 and 3 systems. Protein monomer and domain prediction is largely solved, with barely any space for further improvements at the backbone level although modeling local details, irregular regions, and mutational effects remains challenging. For protein assemblies, AF-based methods, especially when expertly guided or enhanced by servers like those from the Yang, Zheng/Zhang, and Cheng labs, show progress, though complex topologies and antibody-antigen interactions (where specialized docking approaches showed promise) are still difficult. Notably, a priori knowledge of stoichiometry significantly aids assembly prediction. Protein-ligand co-folding with AF3 demonstrated strong potential for pose prediction, outperforming many participants and some dedicated docking tools in baseline tests, but ligand affinity prediction is currently totally unreliable. Nucleic acid structure prediction lags considerably, heavily relying on 3D templates and expert human intervention, with AF3 showing notable limitations. Overall, AF3’s modeling capabilities are at or close to the state of the art on all fronts; additionally, it shows slight improvements over AF2 and more detailed confidence metrics. This article guides users on tool selection, realistic accuracy expectations, and persistent challenges, emphasizing the critical role of confidence metrics in interpreting AI-generated models.