NVIDIA Nemotron 3.5 Embed, Rerank, and Parse: Advancing Agents with World-Class Retrieval Models

An agent’s answers depend on the evidence it can retrieve. When that evidence is incomplete or irrelevant, the agent will repeat searches and inspect additional documents, consuming more inference tokens before completing the task. Better retrieval can help agents reach grounded answers with fewer wasted tokens. At enterprise scale, that waste leads to massive inefficiencies. Agents need the right retrieval models and systems to reach insights faster.

Today, we are expanding the Nemotron Retriever collection with new open, commercially available models for embedding, reranking, and document extraction. The new models include Nemotron 3.5 Embed 8B BF16, 1B BF16, 1B NVFP4, Nemotron 3.5 Rerank 8B BF16, and Nemotron 3.5 Parse at 631M parameters.

Together, these state-of-the-art models bring multimodal retrieval, relevance ranking, and document processing to enterprise agent workflows.

Better Retrieval for More Efficient Agents

Retrieval quality affects both the answers an agent produces and the LLM inference cost. Relevant evidence can help the agent answer a question or better decide its next step, and reranking improves the quality of evidence that enters its context.

Consider an agent asked to identify products at risk of stockouts next quarter and explain why. It must combine demand forecasts in charts, inventory levels in tables, and supplier lead times in reports, then reason about whether replenishment will arrive before inventory runs out. Completing the task requires finding the relevant evidence across documents and using it together in the analysis.

Figure 1. End-to-end task success versus total agent inference tokens per task across retrieval configurations.

Nemotron 3.5 Embed and Rerank

The four models provide different options for retrieval and second-stage ranking:

Table 1. Nemotron 3.5 Embed and Nemotron 3.5 Rerank model capabilities and use cases across various sizes and variants.

Key features include: