Multimodal deep learning is a recent trend in artificial intelligence that uses multiple data modalities such as images, text, video, and audio to understand the real world. OpenAI CLIP model is an open-source vision-language AI model trained using image and natural language data for zero-shot classification tasks. It has several benefits over traditional vision models, including zero-shot learning and better real-world performance. However, it also has limitations such as poor performance on fine-grained tasks and out-of-distribution data. Alternatives to OpenAI CLIP include PubmedCLIP for medical visual question-answering, PLIP for pathological image classification, SigLip for efficient training with extensive datasets, StreetCLIP for geolocation prediction, FashionCLIP for fashion product classification and retrieval, CLIP-RSICD for extracting information from satellite images, BioCLIP for biological research, and CLIPBert for video understanding. These alternatives are suitable for domain-specific tasks and can help users develop advanced multi-modal models to solve modern industrial problems.