In　this　paper,　a　graphical　performance　tool　(GPT)　is　proposed　to　select,　among　several　existing　Predictive　Torque　Control　(PTC)　methods,　an　appropriate　approach　to　drive　a　motor　for　given　specifications.　To　achieve　a　fair　trade-off　evaluation,　switching　frequency,　current,　and　torque　total　demand　distortions　have　been　considered　depending　on　PTC　parameters.　Since,　in　the　conventional　PTC　method,　torque　and　flux　weighting　factors　(WFs)　strongly　influence　the　behavior　of　the　motor.　Thus,　to　avoid　an　unfair　comparison　due　to　a　non-optimal　tuning,　the　proposed　GPT　is　used　to　show　each　performance　criterion　when　parameters　and　operating　points　change.　Simulation　results　of　three　operating　points　show　that　there　is　only　one　sharing　common　zone　where　current　harmonics　and　torque　ripples　are　near　zero.　This　area　has　been　named　the　optimal　tuning　zone　for　PTC　controllers　and　is　used　to　select　their　parameter　values.　After　obtaining　these　desired　parameter　values,　a　trade-off　performance　evaluation　is　made　over　a　wide　operating　range　to　compare　different　PTC　controllers　with　and　without　WF　that　are　proposed　in　the　literature.　©　2019　IEEE.