Multimodal recommendation systems are increasingly popular for their potential to improve performance by integrating diverse data types. However, the actual benefits of this integration remain unclear, raising questions about when and how it truly enhances recommendations. In this paper, we propose a structured evaluation framework to systematically assess multimodal recommendations across four dimensions: Comparative Efficiency, Recommendation Tasks, Recommendation Stages, and Multimodal Data Integration. We benchmark a set of reproducible multimodal models against strong traditional baselines and evaluate their performance on different platforms. Our findings show that multimodal data is particularly beneficial in sparse interaction scenarios and during the recall stage of recommendation pipelines. We also observe that the importance of each modality is task-specific, where text features are more useful in e-commerce and visual features are more effective in short-video recommendations. Additionally, we explore different integration strategies and model sizes, finding that Ensemble-Based Learning outperforms Fusion-Based Learning, and that larger models do not necessarily deliver better results. To deepen our understanding, we include case studies and review findings from other recommendation domains. Our work provides practical insights for building efficient and effective multimodal recommendation systems, emphasizing the need for thoughtful modality selection, integration strategies, and model design.