From Search Results to Evidence: NVIDIA Nemotron 3.5 Embed and Rerank on DeepInfra

DeepInfra has added NVIDIA’s Nemotron 3.5 Embed 1B, Embed 8B, and Rerank 8B models to its API, offering pay-per-token access without deployment setup. Part of the Nemotron Retriever collection, the models are designed to improve agentic retrieval by embedding and searching not only text but also tables, charts, images, diagrams, and complete document pages, while the reranker prioritizes the strongest evidence for a query. The 1B embedding model targets efficient large-scale use, the 8B version emphasizes retrieval quality, and the Rerank 8B model is intended to support iterative, multi-source agent workflows; NVIDIA reports that reranking can reduce agent inference costs by up to 14%. DeepInfra presents the models as suitable for enterprise document search, multi-step research, agent memory, and code retrieval, and provides Python examples for embedding documents, selecting similar results, reranking candidates, and searching page images. Pricing per million input tokens ranges from $0.015 to $0.070 for embedding models depending on model size and modality, while Rerank 8B costs $0.050.

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