The model being discussed focuses on analyzing pieces of video and images to make predictions based on the semantic meaning captured in an embedding space, rather than operating on raw pixels. This approach is similar to Sora, as both operate at the level of the embedding space. The goal of the model is to generate meaningful embeddings from video or image patches through an encoder, capturing the essence or meaning of the inputs.