Misclassification Rate for Random Forest Plateauing too Early
If you and your colleagues ran the same model on the same data you should get the same results (give or take a stochastic error). Did your colleagues use the same environment, same packages and same versions?

Also, it is known that building more trees gives better performance and if possible you should build more not less, as RF does not overfit with more trees, the error / accuracy stabilizes at some point. What that point is (number of trees) varies from data to data, so you cannot really determine this beforehand.