  In this paper, we introduce the $\sigma$-antithetic multilevel Monte Carlo
(MLMC) estimator for a multi-dimensional diffusion which is an extended version
of the original antithetic MLMC one introduced by Giles and Szpruch \cite{a}.
Our aim is to study the asymptotic behavior of the weak errors involved in this
new algorithm. Among the obtained results, we prove that the error between on
the one hand the average of the Milstein scheme without L\'evy area and its
$\sigma$-antithetic version build on the finer grid and on the other hand the
coarse approximation stably converges in distribution with a rate of order 1.
We also prove that the error between the Milstein scheme without L\'evy area
and its $\sigma$-antithetic version stably converges in distribution with a
rate of order $1/2$. More precisely, we have a functional limit theorem on the
asymptotic behavior of the joined distribution of these errors based on a
triangular array approach (see e.g. Jacod \cite{c}). Thanks to this result, we
establish a central limit theorem of Lindeberg-Feller type for the
$\sigma$-antithetic MLMC estimator. The time complexity of the algorithm is
carried out.
