Multimodal models in movies have been a challenging domain due to the scarcity of quality data and the labor-intensive process of data collection and annotation. Traditional methods fall short in analyzing complex video narratives, especially in longer formats like movies. Addressing these issues, MovieLLM introduces a groundbreaking framework that utilizes GPT-4 and text-to-image models to generate detailed scripts and corresponding visuals. This innovative approach not only offers flexibility and scalability but also significantly enhances the performance of multimodal models. By producing synthetic, high-quality data, MovieLLM overcomes the limitations of existing datasets, which often suffer from bias and a lack of diversity. The framework’s effectiveness is backed by extensive experiments, confirming its potential to revolutionize the way machines understand long videos. MovieLLM stands as a testament to the advancements in machine learning, paving the way for more nuanced and comprehensive video analysis.