  In this paper we propose three $p$-th order tensor methods for
$\mu$-strongly-convex-strongly-concave saddle point problems (SPP). The first
method is based on the assumption of $p$-th order smoothness of the objective
and it achieves a convergence rate of $O \left( \left( \frac{L_p R^{p -
1}}{\mu} \right)^\frac{2}{p + 1} \log \frac{\mu R^2}{\varepsilon_G} \right)$,
where $R$ is an estimate of the initial distance to the solution, and
$\varepsilon_G$ is the error in terms of duality gap. Under additional
assumptions of first and second order smoothness of the objective we connect
the first method with a locally superlinear converging algorithm and develop a
second method with the complexity of $O \left( \left( \frac{L_p R^{p - 1}}{\mu}
\right)^\frac{2}{p + 1}\log \frac{L_2 R \max \left\{ 1, \frac{L_1}{\mu}
\right\}}{\mu} + \log \frac{\log \frac{L_1^3}{2 \mu^2 \varepsilon_G}}{\log
\frac{L_1 L_2}{\mu^2}} \right)$. The third method is a modified version of the
second method, and it solves gradient norm minimization SPP with $\tilde O
\left( \left( \frac{L_p R^p}{\varepsilon_\nabla} \right)^\frac{2}{p + 1}
\right)$ oracle calls, where $\varepsilon_\nabla$ is an error in terms of norm
of the gradient of the objective. Since we treat SPP as a particular case of
variational inequalities, we also propose three methods for strongly monotone
variational inequalities with the same complexity as the described above.
