A typical problem for magnetic surveys with small Unmanned Aerial Systems (sUAS) is the heading error caused by undesired magnetic signals that originate from the aircraft. This can be addressed by suspending the magnetometers on sufficiently long tethers. However, tethered payloads require skilled pilots and are difficult to fly safely. Alternatively, the magnetometer can be fixed on the aircraft. In this case, aircraft magnetic signals are removed from the recordings with a process referred to as magnetic compensation, which requires parameters estimated from calibration flights flown in an area with magnetically low-gradients prior to the survey. We present open-source software fully written in Python to process data and compute compensations for two fundamentally different magnetometer systems (scalar and vector). We used the software to compare the precision of two commercially available systems by flying dense grid patterns over a 135 × 150 m area using different suspension configurations. The accuracy of the magnetic recordings is assessed using both standard deviations of the calibration pattern and tie-line cross-over differences from the survey. After compensation, the vector magnetometer provides the lowest heading error. However, the magnetic field intensity recovered with this system is relative and needs to be adjusted with absolute data if absolute intensity values are needed. Overall, the highest accuracy of all suspension configurations tested was obtained by fixing the magnetometer 0.5 m below the sUAS onto a self-built carbon-fiber frame, which also offered greater stability and allowed fully autonomous flights in a wide range of conditions.