NVIDIA Nemotron 3 Embed Takes #1 on RTEB, Targeting Agentic Retrieval Pipelines

NVIDIA has released Nemotron 3 Embed, an embedding model that achieved the top overall ranking on the Retrieval Text Embedding Benchmark (RTEB), which is specifically designed to evaluate models for agentic retrieval scenarios. Unlike general embedding benchmarks, RTEB stress-tests models on multi-hop retrieval, long-context passages, and tool-augmented search — capabilities critical for building reliable RAG pipelines in agentic workflows. The model is available on Hugging Face, making it immediately accessible for developers to drop into existing retrieval stacks. For teams building agents that depend on accurate, context-aware retrieval, this is a meaningful baseline shift — top RTEB performance suggests better real-world grounding compared to previously dominant models. Developers building enterprise RAG or agentic search should benchmark Nemotron 3 Embed against their current embeddings, especially on complex multi-step retrieval tasks.