We consider the problem of filtering partially observed diffusions associated to discrete time observations. This problem requires the development of advanced numerical methods such as particle filters. In this article we develop a new sequential entropic optimal transport filter (EOTF). The main idea is to sample from successive couplings of the filters. To achieve this, we adopt the approach of an entropic optimal transport (EOT) update implemented through a learned Schrödinger potential, in the spirit of the light Schrödinger bridge methodology \cite{korotin2024light}. Based on this, we develop a multilevel version of this filter (ML-EOTF) in which consecutive discretization levels are coupled at the terminal time by a scaled EOT plan with level-dependent regularization $\eps_l=\eps_0 h_l^κ$ with discretization stepsize $h_l$. Under mild conditions, we show that the level-difference estimator with exact Schrödinger potentials has variance of order $N_l^{-1}\{h_l^β+\eps_l\}$, where $β$ is the strong-error for the discretized SDE. The analysis is then extended to learned potentials, with all approximation and optimization errors summarized by a joint Kullback--Leibler discrepancy whose contribution to the mean squared error is explicitly quantified. Combined with a fixed-time weak-error estimate, this yields the canonical multilevel complexity $\cO(δ^{-2}|\logδ|^2)$ to achieve an MSE of $δ^2$ for Euler--Maruyama discretizations. Numerical experiments validate the results.

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