For interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) data, multiple solutions of the respective forward problems are needed. In this paper, we assess performance of the mixed-hybrid finite element method (MHFEM) applied to EEG and MEG modeling. The method provides an approximate potential and induced currents and results in a system with a positive semi-definite matrix. The system thus can be solved with a variety of standard methods (e.g. the preconditioned conjugate gradient method). The induced currents satisfy discrete charge conservation law making the method conservative. We studied its performance on unstructured tetrahedral grids for a layered spherical head model as well as a realistic head model. We also compared its accuracy versus the conventional nodal finite element method (<inline-formula> <tex-math notation="LaTeX">{P}_{1} </tex-math></inline-formula> FEM). To avoid modeling singular sources, we completed our computations with a subtraction approach; the derived expression for the MEG response different from earlier published and involves integration of finite quantities only. We conclude that although the MHFEM is more computationally demanding than the <inline-formula> <tex-math notation="LaTeX">{P}_{1} </tex-math></inline-formula> FEM, its use is justified for EEG and MEG modeling on low-resolution head models where <inline-formula> <tex-math notation="LaTeX">{P}_{1} </tex-math></inline-formula> FEM loses accuracy.