If the semivariogram was different for two exponential models compared to one exponential model, then they must have had different Major Semiaxes (or Minor Semiaxes if you corrected for anisotropy).
When you supply more than one model, the semivariogram curve is generated by a weighted average of the multiple models (where the weights are determined by least-squares).
Using multiple models is beneficial when your data is the result of multiple underlying processes (wind patterns and temperature, for example). Each model can be used to account for a different underlying process. Sometimes these underlying processes are known, and sometimes they aren't. Using multiple models when you don't know the processes that generated your data can be suspect, but if the cross-covariance cloud and empirical semivariogram suggest that different distances follow different semivariograms (for example, one model fits the data up to 1000 meters, and another model fits beyond that), it may be justified to use two models with different search neighborhoods.
