Magnetic tracking algorithms can be used to determine the position and orientation of magnetic markers or devices. These techniques are particularly interesting for biomedical applications such as teleoperated surgical robots or the control of upper limb prostheses. The performance of different algorithms used for magnetic tracking was compared in the past. However, in most cases, those algorithms were required to track a single magnet.Here we investigated the performance of three localization algorithms in tracking up to 9 magnets: two optimization-based (Levenberg-Marquardt algorithm, LMA, and Trust Region Reflective algorithm, TRRA) and one recursion-based (Unscented Kalman Filter, UKF). The tracking accuracy of the algorithms and their computation time were investigated through simulations.The accuracy of the three algorithms, when tracking up to six magnets, was similar, leading to estimation errors varying from 0.06 ± 0.02 mm to 2.26 ± 0.07 mm within a 100 mm × 54 mm × 100 mm workspace, at the highest sampling frequency. In all cases, computation times under 300 ms for the UKF and 45 ms for the LMA/TRRA were obtained. The TRRA showed the best tracking performance overall.These outcomes are of interest for a wide range of robotics applications that require remote tracking.