We present our submission to the LoResMT 2026 Shared Task on Russian-Kyrgyz machine translation. Our approach demonstrates that ensembling diverse translation models with simple consensus-based voting can significantly outperform individual models, achieving a +1.37 CHRF++ improvement over our best single model. Notably, we find that including "weaker" models in the ensemble improves overall performance, challenging the conventional assumption that ensembles should only combine top-performing systems. Our system achieved 49.31 CHRF++ on the public leaderboard and 48.55 CHRF++ on the final private test set, placing 3rd in the Russian-Kyrgyz track using only open-weight models without any fine-tuning on parallel Kyrgyz data. We report several counter-intuitive findings: (1) simple voting outperforms quality-weighted selection, (2) more diverse models help even when individually weaker, and (3) post-processing "corrections" can hurt performance when reference translations contain similar artifacts.