We address a multi-class traffic model, for which we computationally assess the ability of mean-field games (MFG) to yield approximate Nash equilibria for traffic flow games of intractable large finite-players. We introduce a two-class traffic framework, following and extending the single-class lines of \cite{huang_game-theoretic_2020}. We extend the numerical methodologies, with recourse to techniques such as HPC and regularization of LGMRES solvers. The developed apparatus allows us to perform simulations at significantly larger space and time discretization scales. For three generic scenarios of cars and trucks, and three cost functionals, we provide numerous numerical results related to the autonomous vehicles (AVs) traffic dynamics, which corroborate for the multi-class case the effectiveness of the approach emphasized in \cite{huang_game-theoretic_2020}. We additionally provide several original comparisons of macroscopic Nash mean-field speeds with their microscopic versions, allowing us to computationally validate the so-called $ε-$Nash approximation, with a rate slightly better than theoretically expected.