Multimodal learning with next-token prediction for large multimodal models

Developing a unified algorithm that can learn from and generate across modalities such as text, images and video has been a fundamental challenge in artificial intelligence. Although next-token prediction has driven major advances in large language models¹, its extension to multimodal domains has remained limited, and diffusion models for image and video synthesis^2,3 and compositional frameworks that integrate vision encoders with language models⁴ still dominate. Here we introduce Emu3, a family of multimodal models trained solely with next-token prediction. Emu3 equals the performance of well-established task-specific models across both perception and generation, matching flagship systems while removing the need for diffusion or compositional architectures. It further demonstrates coherent, high-fidelity video generation, interleaved vision-language generation and vision-language-action modelling for robotic manipulation. By reducing multimodal learning to unified token prediction, Emu3 establishes a robust foundation for large-scale multimodal modelling and offers a promising route towards unified multimodal intelligence.