The fast development of Quantum Computing (QC), with its innovations and advantages, proposes a challenge for the progress of Quantum Machine Learning models. This is due to the rapidly evolving frameworks such as Qiskit and PennyLane, in addition to the ad-hoc nature of creating quantum circuits. However, as far as we know, there is no framework that allows for the systematic, flexible, and straightforward comparison of QML models. Mindful of this, in this work, we present a novel Python library with the objective to compare and benchmark a great variety of models and characteristics based on different ansatzes and architectures from the literature.