Accurate modeling of shoreline changes is crucial for the sustainable management of coastal areas. To evaluate the state-of-the-art in shoreline modeling, a workshop, ShoreShop2.0, was initiated to benchmark existing shoreline models through blind testing. A total of 34 models were applied to predict shoreline positions at a concealed site, referred to as Beach X, and their performance was objectively evaluated against hidden data. Despite significant variations in model performance, the best-performing models provided coherent predictions regardless of the model type. The evaluation results also highlight the applicability of these models to data-poor regions. Using global, open-source datasets as inputs, the best-performing models achieved prediction accuracies comparable to the intrinsic error of the shoreline data. This collaborative benchmarking effort not only highlights the current strengths and limitations of shoreline models but also provides valuable insights and directions for future research and development in the field.