You should have a look at the matlabFunction function of the Symbolic Math Toolbox, which performs exactly this and works quite well for reasonably scaled functions (but here's a counterexample). When facing a mixed problem (needing symbolic math, but also computation-expensive evaluation of the results), it might be most practical to solve your initial problem in a parametric (symbolic) way once, then to use the very last result by turning it into an appropriate numerical function.

Whenever possible, one should prefer numerical MATLAB functions, especially those can often be extended to work in a vectorized way, along array inputs to provide array output. However, the symbolic engine is designed to be smart, not to be fast (after all, sooner or later the symbolic engine must perform the evaluation of the function, but MATLAB was designed to be efficient with numerical problems). This can be very useful for tackling mathematical problems such as exact differentiation, integration, working with arbitrary-precision arithmetic, or solving equations. However, using the Symbolic Math Toolbox one may use symbolic expressions and functions. By default MATLAB variables and functions are numeric, and this is why in your linked discussion only inline and anonymous functions are compared. Basic MATLAB functionality is designed for numerical calculations, i.e.

Matlab symbolic function