Harnessing the computational power of large-scale models while managing their efficiency remains a critical challenge in machine learning. Introducing a token-level recurrent router offers a novel solution to optimize the distribution of computational resources among experts, dynamically enhancing both the precision of expert selection and the overall performance of the model. By implementing this adaptive routing mechanism within the Mistral 8x7b Mixture-of-Experts (MoE) model, the study achieves substantial improvements in routing accuracy, scalability, and computational efficiency. The recurrent router's ability to adjust its routing decisions based on evolving token context allows it to handle complex linguistic structures and long-range dependencies more effectively, providing a balanced and efficient use of computational resources. Comprehensive evaluations demonstrate that integrating the router leads to enhanced accuracy across a range of NLP tasks, reduces computational overhead, and facilitates the model's scalability to accommodate more parameters without a significant increase in resource consumption. These findings highlight the potential of token-level recurrent routing to advance the capabilities of MoE models, making them more adaptable and efficient for diverse natural language processing applications.