Title: Multimodal Open-Vocabulary Video Classification via Vision and Language Models. Abstract: Utilizing vision and language models (VLMs) pre-trained on internet-scale image-text pairs is becoming a promising paradigm for open-vocabulary vision tasks. This work conducts an extensive study for multimodal open-vocabulary video classification via pre-trained VLMs by leveraging motion and audio that naturally exist in the video. We design an asymmetrical cross-modal fusion mechanism to aggregate multimodal information differently for video and optical flow / audio. Experiments on Kinetics and VGGSound show that introducing more modalities significantly improves the accuracy on seen classes, while generalizing better to unseen classes over existing approaches. Despite its simplicity, our method achieves state-of-the-art results on UCF and HMDB zero-shot video action recognition benchmarks, significantly outperforming traditional zero-shot techniques, video-text pre-training methods and recent VLM-based approaches. Code and models will be released.