In this paper, we present 'TudoBonus,' a pioneering multi-modal benchmark dataset specifically designed to address the challenges of vision recognition tasks in the context of the circular economy. Comprising over 109,000 diverse data points, including product images, SKU codes, brands, types, departments, object bounding boxes, and object descriptions, 'TudoBonus' offers a resource to drive innovation in sustainable, circular practices.To assess the complexity of 'TudoBonus,' we performed baseline experiments using pretrained models such as ResNet, ViT, DINOv1-v2, and visual-language models like CLIP and ALIGN. Our results show that even state-of-the-art methods struggle to perform classical tasks effectively with this dataset, revealing a gap between current models and the complex demands of the circular economy environment.These findings indicate that there is significant room for improvement, and we believe the diversity and quality of the 'TudoBonus' dataset will open up new avenues for research, encouraging the development of more advanced models tailored to the challenges of computer vision and automation in the circular economy, contibuting to more sustainable industries and a green economy.