Here we employ SwarmCG , an automatic multi-objective optimization approach that facilitates the development of transferable lipid FFs, to evaluate and compare the potential of two putative refined CG representations of the Martini lipids. Using simultaneously in the training sets 8 PC lipids simulated at different temperatures and spanning different phase states, we optimize the bonded parameters in building blocks of the lipid models while non-bonded parameters remain constant (set to Martini 3.0.0 4 ). Applying SwarmCG here guarantees that an optimal set of bonded parameters has been found for each resolution compared, according to a given set of molecular modeling constraints (i.e. composition of the training set, non-bonded parameters and a limited number of other simulation parameters). This eliminates uncertainties related to parameters tuning, which in turn allows to gain insights on the relative ability of each CG representation to further enhance the thermodynamic properties of the lipid models in the Martini framework. The two putative CG representations are compared a posteriori using a range of temperatures and by simulating a portion of the phase diagram of a DOPC/DPPC mixture known to simultaneously exhibit two phase states experimentally .