Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities. This paper studies the following question: Can we augment CLIP training with task-specific vision models from model zoos to improve its visual representations? Towards this end, we leverage open-source task-specific vision models to generate pseudo-labels for an uncurated and noisy image-text dataset. Subsequently, we train CLIP models on these pseudo-labels in addition to the contrastive training on image and text pairs. This simple setup shows substantial improvements of up to 16.3% across different vision tasks, including segmentation, detection, depth estimation, and surface normal estimation. Importantly, these enhancements are achieved without compromising CLIP's existing capabilities, including its proficiency in promptable zero-shot classification. Foundation Models (FMs) are revolutionizing different domains of artificial intelligence and machine learning, including computer vision (Radford et al., 2021; He et al., 2022; Kirillov et al., 2023b) and natural language processing (Devlin et al., 2018; Brown et al., 2020; Touvron et al., 2023). FMs can be trained on web crawled data without relying on crowd or expert annotations, and yet they demonstrate strong generalization capabilities (Jia et al., 2021; Schuhmann et al., 2022).