NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval

Published July 16, 2026

Retrieval is critical in multi-step agentic workflows where poor retrieval can cause agents to fetch irrelevant context, re-query, waste token budget, and carry noise into later reasoning steps.

Today, we are releasing NVIDIA Nemotron 3 Embed, a collection of open and commercially available embedding models designed to improve retrieval quality while giving developers practical deployment options for production-scale RAG, agentic retrieval, code retrieval, and agent memory.

The collection includes three open models that achieve state-of-the-art retrieval across the accuracy-efficiency curve, led by an 8B model that tops the RTEB leaderboard and efficient 1B variants built for production-scale deployment:

Model	Role	Best for
Nemotron-3-Embed-8B-BF16	Flagship Quality Anchor: The flagship embedding model, ranking #1 on RTEB.	Precision-critical retrieval and high-stakes enterprise RAG
Nemotron-3-Embed-1B-BF16	High-Efficiency Standard: A high-efficiency model for production retrieval where latency and cost matter.	Cost- and latency-sensitive production serving
Nemotron-3-Embed-1B-NVFP4	Hardware-Accelerated Variant: A Blackwell-optimized variant for high-throughput retrieval with a smaller memory footprint.	Ultra-high-throughput and massive-scale infrastructure

Table 1. Nemotron 3 Embed Model Usability and Deployment Matrix.

Figure 1. RTEB Multilingual Leaderboard screenshot (July 15, 2026) showing Nemotron-3-Embed-8B-BF16 ranked as #1.

Key Features

Beyond the RTEB result, Nemotron 3 Embed introduces a production-ready feature set for enterprise r

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