Flexible slender structures, such as cables, are integral components of engineering systems, including automotive applications and advanced robotic platforms. Their dynamic behavior is characterized by large deformations and finite rotations, and the resulting response is strongly influenced by internal dissipation mechanisms that vary across excitation frequencies. Accurate capturing of damping is essential for reliable prediction of the dynamic response under broadband excitation. Geometrically exact Cosserat rod formulations, combined with quaternion-based representations of finite rotations, provide a structured framework for the simulation of slender structures. These formulations enable a consistent description of extension, shear, bending, and torsion within a unified framework [1]. In many Cosserat rod models, damping is described using the linear Kelvin–Voigt (KV) viscoelastic law, which offers a simple and effective representation of damping behavior over limited excitation ranges. However, due to its single relaxation timescale, this model is primarily suited to narrow-band dynamic responses and may not fully represent the frequency-dependent dissipation observed in broadband scenarios. More general viscoelastic laws, such as the Generalized Maxwell (GM) formulation, provide a multi-timescale representation of dissipation and are well established in structural dynamics [2]. In this study, the geometrically exact Cosserat rod model is extended by embedding the constitutive law of the GM without altering the underlying formulation. The dynamic responses obtained from the simulations, using both KV & GM models, are compared against experimental measurements from controlled dynamic tests employing constant, linear, and quadratic frequency chirp excitations. The viscoelastic material parameters are identified by optimization against the experimental data, providing a basis for model evaluation. The results show that the GM formulation consistently outperforms the KV model in representing damping under broadband excitation, yet it remains insufficient to capture the observed dynamic response. This persistent gap calls for the exploration of data-driven and machine-learning-based constitutive modelling approaches to further enhance predictive performance.