The rapid growth in the scale and complexity of robotic arm systems, whether deployed individually or in multi-agent networks, creates significant obstacles for real-time control, simulation, and practical implementation. This work proposes an integrated model order reduction framework that unifies positive-real balanced truncation for single-agent robots with balanced truncation and clustering strategies for large-scale multi-agent configurations. Using a benchmark four-degree-of-freedom manipulator and a cyclic network of six agents, all methods are systematically implemented and evaluated in MATLAB. Reduced-order models are constructed for a range of target orders, and their fidelity is assessed through H₂ and H∞ error norms, as well as time- and frequency-domain analyses. Results demonstrate that, for individual robotic arms, reducing the model order to four preserves key dynamic behaviors with minimal loss (H₂, H∞ errors below 0.01), while further reduction increases the error but may remain acceptable for less demanding applications. For multi-agent networks, model reduction from order 48 to 16 incurs virtually no loss of accuracy, and even highly compact models (orders 2–4) closely approximate the full system in most scenarios. The approach achieves significant simplification without compromising stability or passivity, making it well-suited for efficient controller synthesis and real-time deployment. These findings offer practical, evidence-based guidance for engineers and researchers seeking scalable and reliable model reduction in advanced robotic systems.