Predicting in vivo transcription factor (TF) binding remains challenging due to interactions with cofactors and dynamic chromatin remodeling that modulate site accessibility. In this regard, accurate characterization of TF binding loci is essential for predictive performance. We introduce EpiBinder, a multimodal deep neural network that augments base-resolution DNA sequence models with single-nucleotide epigenetic information—cytosine methylation levels from whole-genome bisulfite sequencing and DNase I hypersensitivity—to improve in vivo TF binding predictions. Trained on human cell-lines, EpiBinder demonstrates that integrating epigenetic information in a cell-type–specific manner reduces the epistatic uncertainty that current sequence-only DNA models suffer. Our model achieves up to a 15-point gain in area under the precision–recall curve compared the state-of-the-art. Our code is publicly available at \href{https://anonymous.4open.science/r/EpiBinder-0BB4/README.md}{Anonymous-GitHub}.