In this paper, we study the performances of the NEW Unconstrained Optimization Algorithm (NEWUOA) with different numbers of interpolation points. NEWUOA is a trust region method, the number of points used to build the surrogate model is an input parameter of the algorithm. We compare the performances of NEWUOA using three different number of points in search spaces of dimension from two to forty on problems from the BBOB 2009 noiseless function testbed. In particular we study the performances of an `average' number of interpolation points that scales like the dimension of the search space to the power 3/2. Using this number of interpolation points is expectedly faster than using the maximum number of interpolation points (scaling like the square of the dimension), though it does not grant better performances than using a number of interpolation points scaling like the dimension.