In the rapid advancement of artificial intelligence applied to natural language processing, the quality of text vector representations —known as embeddings— is decisive for the performance of any system. However, measuring that quality in languages other than English has traditionally been a challenge. Until now, those working with Brazilian Portuguese had to rely on translated benchmarks or multilingual coverages that do not reflect the lexical richness or cultural particularities of native Portuguese. This gap motivated the creation of MTEB-PT, a benchmark that brings together 22 authentic Brazilian tasks —from classification and clustering to information retrieval and re-ranking— evaluating 93 models ranging from 23 million to 27 billion parameters. The initiative demonstrates that an open-source model can compete at the highest level without relying on commercial APIs, and reveals that the global multilingual ranking only moderately predicts (Spearman's rho 0.75) actual performance in Portuguese. For companies integrating these technologies into their custom applications, having such a local benchmark allows informed decision-making and avoids surprises when deploying solutions in the Brazilian market.
The relevance of MTEB-PT goes beyond academia: it provides a solid foundation for those developing custom software with natural language components. By analyzing the results, it is observed that retrieval and re-ranking tasks are the most discriminating among models, while semantic similarity tasks tend to group them with smaller differences. This information is valuable when designing internal search systems, chatbots, or recommendation engines. A company wishing to implement AI for business can rely on this data to select the most suitable embedding without needing costly APIs, as the study confirms that accessible open-weight models reach the top tier. At Q2BSTUDIO, as a technology-specialized firm, we integrate these findings into our developments, offering AWS and Azure cloud services that facilitate the scalable deployment of such models. Furthermore, we understand that performance optimization depends not only on the embedding but also on the overall architecture, so we combine this foundation with cybersecurity techniques and AI agents to ensure robust solutions.
One of the key learnings from the benchmark is the need to evaluate in a native context: a model ranked third globally can drop to forty-ninth in Portuguese. This underscores why companies cannot rely solely on international rankings when defining their business intelligence services strategy. For example, when building a Power BI dashboard that consumes textual data in Portuguese, the choice of embedding directly impacts the accuracy of reports. Therefore, at Q2BSTUDIO, we recommend testing with benchmarks like MTEB-PT before integrating any model. Our experience in artificial intelligence allows us to advise clients on selecting the optimal embedding, as well as creating custom applications that leverage these capabilities. Additionally, the ability to host proprietary models on cloud infrastructures avoids dependence on third parties, a critical factor for regulated sectors. In short, MTEB-PT marks a before and after for Portuguese language processing, and from a business perspective, having such tools is the first step toward truly effective and sovereign AI solutions.
