💡 The paper presents TorchUMM, a unified codebase for evaluating and analyzing multimodal models that can understand, generate, and edit across visual and textual modalities. The development of unified multimodal models has been hindered by the diversity of model architectures and training paradigms, making it challenging to compare and analyze different models. To address this issue, TorchUMM provides a comprehensive framework that supports a wide range of models and tasks, including multimodal understanding, generation, and editing. The codebase includes a broad spectrum of models and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models, facilitating the development of more capable unified multimodal systems. The codebase is designed to foster deeper insights into the strengths and limitations of different models, and its availability is expected to accelerate the development of unified multimodal models. The TorchUMM codebase is available for use, allowing researchers to evaluate and analyze their models in a standardized and reproducible manner. Overall, the paper contributes to the development of unified multimodal models by providing a unified framework for evaluation, analysis, and post-training, which can help to advance the field of multimodal modeling.