NVIDIA

Nemotron 3.5 Parse

About

Nemotron 3.5 Parse is an open-weight model from NVIDIA. Nemotron 3.5 Parse (0.6B): page to Markdown with HTML tables, or `layout` for every block's class and box. It accepts text, images, and documents. Context window is 16K. Released September 3, 2026.

Context window
16K
Tokens of context on a request.
License
—
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.10
Cached input / 1M	$0.02
Output / 1M	$0.20

Compare

Nemotron 3.5 Parse is one of the catalog's OCR models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
Nemotron 3.5 Parse	Nemotron 3.5 Parse (0.6B): page to Markdown with HTML tables, or `layout` for every block's class and box.	$0.10 / 1M input	—	16K
Florence-2	Vision foundation model for captioning, OCR, detection, and region tasks.	$0 / 1M input	232M	—
DeepSeek-OCR-2	DeepSeek OCR 2 and markdown extraction.	$0.25 / 1M input	3.4B	8K
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	24K

Benchmarks

No published eval is on file for this model.

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	No published score on file.
Cost and performance	Lower listed rate, mid-pack latency

Examples

Invoice to Markdown
markdown

Try it in the OCR playground

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
markdown	The page as Markdown
layout	Layout regions

Estimate cost

Estimate. A page is 2,500 input tokens and 1,000 output tokens.

Pages

$0.45

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="nvidia/nemotron-3.5-parse",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is happening in this image?"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"}},
            ],
        }
    ],
)

print(response.choices[0].message.content)