We introduce DepthCLIP3D, a unified framework designed to tackle a range of open-domain monocular 3D vision tasks, including classification, segmentation, and multi-modal identification. Our core insight is that these diverse tasks can be efficiently handled using a single architecture by integrating the CLIP model with depth maps. Unlike existing approaches that are based on point clouds, depth maps provide a regular grid structure that is well-suited for monocular inputs, allowing the effective use of advanced neural networks such as CNN or ViT. We have developed several best practices into DepthCLIP3D, significantly enhancing its performance and generalization capabilities. Through extensive experiments,we demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple tasks. The project page is available at: DepthCLIP3D.