Since its invention in 1974, the Rubik’s Cube puzzle fasci-nates people of all ages. Its rules are simple: the player getsa scrambled cube and rotates the six faces until each facecontains only stickers of one color. Nevertheless, finding ashort sequence of rotations to solve the cube is hard. Wepresent the first model of Rubik’s Cube for general problemsolvers. To obtain a concise model, we require conditional ef-fects. Furthermore, we extend counterexample-guided Carte-sian abstraction refinement (CEGAR) to support factored ef-fect tasks, a class of planning tasks with a specific kind ofconditional effects which includes Rubik’s Cube. Finally, weevaluate how newer types of abstraction heuristics compareagainst pattern database (PDB) heuristics, the state-of-the-artfor solving Rubik’s Cube. We find that PDBs still outper-form the more general Cartesian and merge-and-shrink ab-stractions. However, in contrast to PDBs, Cartesian abstrac-tions yield perfect heuristics up to a certain problem difficulty.These findings raise interesting questions for future research.