The rapid growth in the scale and complexity of language models has brought about significant challenges in terms of computational efficiency, memory usage, and the occurrence of hallucinations during text generation. Addressing these issues requires a novel approach to streamline token processing without compromising accuracy or coherence in generated outputs. Adaptive Neural Token Streaming (ANTS) introduces a dynamic token filtering mechanism, which adjusts the token input based on contextual importance, thereby optimizing the allocation of computational resources. Through this mechanism, substantial improvements were observed in inference speed, model accuracy, and a marked reduction in hallucinations, while simultaneously reducing memory and energy consumption. Experiments conducted on a recent open-source language model demonstrated that ANTS not only enhances performance but also ensures that models can operate efficiently even under resource constraints. This innovation in token-level optimization has the potential to reshape how language models handle large input sequences, leading to more sustainable, scalable solutions for a wide range of tasks. The research outcomes indicate that token filtering techniques such as ANTS can contribute to the development of more robust and resource-conscious models, thereby pushing the boundaries of what language models can achieve in real-world applications.