Nemotron 3.5 Embed 1B

Embeddings

nemotron-3.5-embed-1b

Nvidia|Created Oct 22, 2026|32.8k context

Embeddings

Nemotron-3.5-Embed-1B-BF16 is a compact multimodal embedding model from NVIDIA that maps text, images, and page screenshots into a shared 2048-dimensional vector space for visual document retrieval and semantic search. It pairs the Nemotron-3-Embed-1B text encoder with a vision encoder, supports up to 32k tokens and Matryoshka dimensions, and suits multilingual RAG over PDFs, slides, and scanned documents.

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Endpoints and request shape

This page collects the public integration surface for the model: supported endpoints, available request parameters, and example calls through the NagaAI API.

Embeddings

Code Example

Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.

Basic request example. Ensure API key permissions. For more details, see our documentation.

from openai import OpenAI

client = OpenAI(base_url="https://api.naga.ac/v1", api_key="YOUR_API_KEY")

resp = client.embeddings.create(
    model="nemotron-3.5-embed-1b",
    input=[
        "The food was delicious!",
        "Service could be faster."
    ],
)
print(resp)