In practical robotic applications, kinematic models are often partially known or entirely unavailable due to structural uncertainties, unmodeled dynamics, or manufacturing variations. As a result, conventional model-based controllers struggle to generalize across diverse robotic platforms. To address these challenges, model-free control strategies such as radial basis function neural network (RBFNN), zeroing neural dynamics (ZND), and gradient neural dynamics (GND) have been developed. This paper presents a comparative study of these three methods for model-free kinematic control of robotic manipulators with high degrees-of-freedom (DoFs). We systematically analyze their theoretical foundations, adaptation mechanisms, and control performance under identical experimental conditions. Numerical simulations on a 7-DoF Franka Emika Panda robot show that all controllers successfully complete the same path-tracking task, with ZND achieving the lowest end-effector position error, followed by RBFNN and GND. Physical experiments further confirm the feasibility and robustness of these controllers under sensing noise and dynamic uncertainties. The results provide clear insights into the trade-offs between tracking accuracy, adaptation speed, and computational efficiency, offering practical guidance for selecting model-free controllers in diverse robotic applications.