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.

Overview Specifications Activity Performance Uptime Examples API Reference

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Pricing-50%

Pay-as-you-go rates for this model. More details in the billing guide.

Tokens (1M)

$0.0075

Capabilities

Input Modalities

Text

Output Modalities

Embeddings

Usage Analytics

Token usage of this model on our platform

Not enough activity data to display a chart

Throughput

Not enough throughput data

Time-To-First-Token (TTFT)

Not enough TTFT data

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)