What are you trying to do? I'm not used to saying one variable has greater importance to predicting the result than another; if they're important, then they're all equally important I like to think.

Are you interested in comparing variables to determine if they should be left out? I think that you should also look if the extra predictors are significant (measured by their p value, or something like gender/age which should almost always be included in a model).

I was wondering if you were trying to compare a situation with 3 predictors (r2=0.76) vs a situation with 4 predictors (r2=0.80), and you would then take the model with 3 predictors because it has a "high enough r2", like trying to get a high enough r2 with as few predictors as possible?