Multicolinearity & accurate weights of predictors
If you build separate models, you are making the internal predictors/features independent of each other. This will cause a lot of your internals predictors to get really high weights which would probably not be the case if you added them together in the same model. An obvious method is to remove correlated predictors and then see what weights you get.