Multimodal magnetic resonance (MR) imaging has revolutionized our understanding of the human brain. However, various limitations in clinical scanning hinder the data acquisition process. Current medical image synthesis techniques, often designed for specific tasks or modalities, exhibit diminished performance when confronted with heterogeneous-source MRI data. Here we introduce a Text-guided Universal MR image Synthesis (TUMSyn) generalist model to generate text-specified multimodal brain MRI sequences from any real-acquired sequences. By leveraging demographic data and imaging parameters as text prompts, TUMSyn achieves diverse cross-sequence synthesis tasks using a unified model. To enhance the efficacy of text features in steering synthesis, we pre-train a text encoder by using contrastive learning strategy to align and fuse image and text semantic information. Developed and evaluated on a multi-center dataset of over 20K brain MR image-text pairs with 7 structural MR contrasts, spanning almost entire age spectrum and various physical conditions, TUMSyn demonstrates comparable or exceeding performance compared to task-specific methods in both supervised and zero-shot settings, and the synthesized images exhibit accurate anatomical morphology suitable for various downstream clinical-related tasks. In summary, by incorporating text metadata into the image synthesis, the accuracy, versatility, and generalizability position TUMSyn as a powerful augmentative tool for conventional MRI systems, offering rapid and cost-effective acquisition of multi-sequence MR images for clinical and research applications.