Text embeddings are central to modern natural language processing, enabling several downstream tasks. Despite their significance, existing evaluation frameworks primarily target English and other high-resource languages, leaving critical gaps for languages such as Turkish. To address this, we present TR-TEB (Turkish Text Embedding Benchmark), the first comprehensive, standardized, and reproducible benchmark for Turkish text embeddings. TR-TEB spans five core task categories: classification, pair classification, clustering, retrieval, and semantic textual similarity. It is supported by a diverse dataset portfolio that integrates 14 curated open-source resources, 26 high-quality translated datasets, and 7 newly constructed Turkish-specific datasets designed to capture the language’s unique characteristics. We test our framework by comparing 45 well-known open-source embedding models. As the first unified evaluation suite, TR-TEB serves as a core tool for the Turkish embedding research community, establishing a systematic basis for model comparison and improvement. Furthermore, its benchmarking methodology and dataset creation process provide a blueprint for extending robust embedding evaluation to other low-resource languages.