Compared how? Architecturally? In training methods? In performance?

The remarkable thing about this modern "full VLA" approach is that it doesn't train for an explicit "world model" at all. There's no factored world state, no pool of trajectory rollouts being sampled from. Instead, everything is rolled into the same VLA stack that does all of it internally.

The world model exists, but it's implicit - the VLA learns one, but only because it helps its training objectives. Trajectories are planned ahead sometimes, also implicitly, for the same reasons.

It's a very "bitter lesson" way of doing things.

As far as I know, Tesla's stack is somewhat similar architecturally - a highly integrated end to end model - but the training process for FSD does include factored "world modeling" side objectives to force the stack to develop driving-relevant internal world representations. So it's in a middle ground between a classic "world model" system and this flavor of VLA - albeit surprisingly close to the latter.

Whether the world modeling side objectives are principled and justified or a leftover from the days of insufficient scale is unclear.