The complexity of modern electrical and electronic systems necessitates effective model order reduction (MOR) techniques to enhance computational efficiency and manageability. This paper evaluates several MOR methods, including Iterative Rational Krylov Algorithm (IRKA), Proper Orthogonal Decomposition (POD), Modal Truncation (MT), Balanced Truncation (BT), and Positive-Real Balanced Truncation (PRBT), applied to a large-scale RLC electrical network. Each method’s performance was assessed through frequency and impulse response analyses. Results indicate that IRKA provides superior accuracy in both frequency and time domains, making it the most reliable for high-fidelity applications. POD shows strong time-domain performance but higher frequency-domain errors, while MT exhibits significant inaccuracies. BT offers a balanced approach, ensuring stability with moderate errors, and PRBT, despite preserving stability and passivity, has higher time-domain errors. This comparative analysis aids in selecting suitable MOR techniques, balancing accuracy, stability, and computational efficiency.