When you say you "know the first model with only 2 factors are more accurate", what do you mean? How do you know? Because Stepwise said so??
A web search on "problems with stepwise regression" will quickly detail many technical reason to distrust Stepwise. Not just JMP's...any software...
My favorite quote about Stepwise procedures: "Personally, I would no more let an automatic routine select my model than I would let some best fit procedure pack my suitcase." Ronan Conroy.
Part of the problem is that Stepwise sets the parameter estimate of the Unchosen factors to zero, so the settings of those factors don't even matter in the Stepwise model. Is that legitimate in your application? This can cause significant bias. Why not use the full model? If you're unhappy with the full model for some legitimate reason, I would recommend using some penalized regression technique like the lasso or elastic net in the Generalized Regression platform. At least those methods constrain the parameters and deal with multicollinearity (unlike Stepwise).