Vectara Multilingual Reranker v1 is a state-of-the-art reranker that enables impressive zero-shot performance on unseen data and domains, supporting over 100 languages in both multilingual and cross-lingual settings. It uses cross-encoders to assign relevance scores to documents given specific queries, improving retrieval performance across various domains, including English-only and multilingual scenarios. The model outperforms industry leaders like Cohere and surpasses the best open-source models, providing blazing-fast inference at minimal cost. Vectara's Multilingual Reranker is designed to be highly scalable, with low latency and variance, making it suitable for real-world applications. The model also includes a feature to set query-independent score thresholds, allowing users to filter out bad results and prioritize relevant information. The performance improvements are demonstrated through experiments comparing the model against other open-source and commercial rerankers, showcasing its potential to enhance AI system performance across diverse fields.