While mixed-language querying is ubiquitous in multilingual communities, the sensitivity of dense retrievers to such queries remains poorly understood.We present a ratio-controlled study on mMARCO that systematically evaluates retrieval performance by varying the mixing proportion of parallel query translations via embedding-level mixing-constructing mixed queries as an interpolation of monolingual embeddings.Experiments with BGE-M3 demonstrate that an optimal mixing ratio outperforms the best monolingual endpoint in 88/105 cases.We uncover a distinct asymmetry driven by English dominance: mixing is uniformly beneficial when retrieving from non-English document indices, whereas indices containing English are best served by pure English queries.Furthermore, English acts as the strongest mixing partner for every non-English document language.Finally, when controlling for English dominance, mixing gains correlate negatively with typological distance.We conclude that language-mix sensitivity is structured and predictable, and we validate the robustness of these patterns across model families and scales. 1