Introducing RTEB: A New Standard for Retrieval Evaluation

Back to Articles Introducing RTEB: A New Standard for Retrieval Evaluation Published October 1, 2025 Update on GitHub Upvote 135 +129 Frank Liu fzliu Follow MongoDB Kenneth C. Enevoldsen KennethEnevoldsen Follow mteb Solomatin Roman Samoed Follow mteb Isaac Chung isaacchung Follow mteb Tom Aarsen tomaarsen Follow mteb Fődi, Zoltán fzoll Follow MongoDB TL;DR – We’re excited to introduce the beta version of the Retrieval Embedding Benchmark (RTEB), a new benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. Existing benchmarks struggle to measure true generalization, while RTEB addresses this with a hybrid strategy of open and private datasets. Its goal is simple: to create a fair, transparent, and application-focused standard for measuring how models perform on data they haven’t seen before. The performance of many AI applications, from RAG and agents to recommendation systems, is fundamentally limited by the quality of search and retrieval. As such, accurately measuring the retrieval quality of embedding models is a common pain point for developers. How do you really know how well a model will perform in the wild? This is where things get tricky. The current standard for evaluation often relies on a model's "zero-shot" performance on public benchmarks. However, this is, at best, an approximation of a model's true generalization capabilities. When models are repeatedly evaluated against the same public datasets, a ga...