Large-scale models for language tasks have demonstrated remarkable capabilities in generating and understanding text across multiple domains, yet challenges remain in efficiently processing complex syntactic structures and handling long-range dependencies. The novel concept of Dynamic Token Hierarchies (DTH) offers a significant advancement by introducing a multitiered token processing framework that dynamically adjusts token representations based on contextual and semantic relevance, optimizing both performance and computational resource allocation. Through a series of experiments, the DTH-enhanced model outperformed traditional models, demonstrating substantial improvements in accuracy, efficiency, and the ability to process linguistically intricate tasks. The implementation of DTH reduced memory usage and processing time while improving task-specific outcomes such as classification and named entity recognition. This innovative framework provides a robust solution to the scalability challenges associated with complex language models, proving its efficacy in balancing computational efficiency with enhanced linguistic comprehension.