Comparing SMOTE to down sampling the majority class in imbalanced binary classification
It's difficult to say without the actual data.

However, I can tell you that SMOTE creates artificial instances, hence, when used in much expanse can 
"deviate" from the actual minority class data. It's difficult to determine the expanse. Many factors take place, firstly the Data, then the neighbouring coefficients. 

P.S. You could try boosting using many random under Samples. Hence, instead of Random Forest you could try first Adaboost for instance were each classifier is trained on a different sub sample.