The Next Generation Science Standards (NGSS) have spurred renewed interest in the epistemologies that students adopt as they engage in science practices. One framework for characterizing students' epistemologies is the "epistemologies in practice" framework (Berland et al. (2016), "Journal of Research in Science Teaching," 53(7), 1082-1112), which focuses on students' meaningful use of four epistemic considerations: Nature, Generality, Justification, and Audience. To date, research based on the framework has primarily examined students' use of the epistemic considerations in the context of diagrammatic modeling. However, with computational technologies becoming more prevalent in science classrooms, the framework could be applied to investigate students' engagement in computational modeling. Moreover, computational modeling could be particularly beneficial to a fast-growing population of multilingual learners (MLs) in the U.S. K-12 context, who benefit from leveraging multiple meaning-making resources (e.g., code, dynamic visualization). This study examined MLs' meaningful use of four epistemic considerations in the context of computational modeling in an elementary science classroom. Fifth-grade MLs (N = 11) participated in two interviews about computational models they had developed as part of two NGSS-designed instructional units that integrated computational modeling (in addition to other model types). Findings indicated that, while students used all four epistemic considerations across the interviews, some considerations (Nature and Generality) were used more frequently than others (Justification and Audience). Beyond diagrammatic modeling, "computational" modeling offered unique affordances for MLs to meaningfully use the considerations as well as to communicate this use, though not without some emergent challenges. Overall, this study highlights the promise of computational modeling for providing a rich sense-making and meaning-making context for MLs to use epistemic considerations. The study also highlights the importance of attending to both epistemic and linguistic aspects of MLs' science learning as well as the potential of interdisciplinary research for studying this learning.