The Maximum Cardinality Matching (MCM) \(G_n\) series represents a well-established set of problems designed to evaluate the capabilities of optimization heuristics. These problems have been instrumental in probing the optimization potential of various DWave quantum computers, offering insights into their performance and limitations. Complementary to the NP-hard yet unconstrained MaxCut problem used in the Q-Score benchmark, the MCM \(G_n\) series provides a constrained series of problems that further challenge quantum optimization techniques. In this paper, we introduce the G-score benchmark specifically tailored to assess the capabilities of quantum optimization heuristics such as Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) or any other heuristic that can be relevant on quantum computers. This score aims to offer a comprehensive metric for evaluating the effectiveness of these methods in solving complex optimization problems.