The expansion of transformer-based architectures has introduced challenges in balancing computational efficiency with task-specific performance, particularly as models grow in scale and complexity. Contextual gradient pruning offers a novel solution through the dynamic selection of parameters based on gradient magnitudes, ensuring that critical linguistic and semantic elements are retained while reducing computational overhead. The methodology introduces a sparsification objective function that aligns parameter reduction with evolving training dynamics, enabling adaptability without compromising representational capacity. Experiments conducted on a state-of-the-art open-source language model demonstrate the method's ability to maintain robust performance across perplexity, token prediction accuracy, and downstream task benchmarks. Results reveal that computational efficiency improves significantly, with reductions in floating-point operations exceeding 60% at higher sparsity levels, while linguistic coherence and rare word coverage remain largely preserved. Comparative analysis highlights the superiority of gradient-informed pruning over conventional static approaches, showcasing its scalability and robustness across diverse architectures. Scalability is further evidenced through consistent improvements in inference latency, energy consumption, and model transferability, making the approach suitable for both small-scale and resource-intensive deployments. The analysis of adversarial robustness and attention head utilization provides deeper insights into the model's adaptive capabilities under varying sparsity conditions. Additionally, the method demonstrates potential in enabling broader accessibility to language models through its ability to tailor sparsity configurations to specific computational environments. The findings position contextual gradient pruning as a transformative advancement in efficient language model optimization, addressing key challenges associated with sustainability and scalability. By combining theoretical rigor with practical efficacy, the study contributes a significant step forward in the pursuit of more adaptable and resourceconscious natural language processing systems.