rednote-hilab

dots.mocr

About

dots.mocr is a 3B open-weight model from rednote-hilab. Multilingual document layout parsing and markdown OCR. It accepts text, images, and documents. Context window is 32K. License is Apache-2.0. Released March 19, 2026.

Parameters
3B
Published parameter count.
Context window
32K
Tokens of context on a request.
License
Apache-2.0
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	$0.20
Cached input / 1M	$0.03
Output / 1M	$0.40

Compare

dots.mocr 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
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	32K
PP-OCRv6	PaddleOCR PP-OCRv6 medium text detection and recognition; scene OCR JSONL on image chat; text returns plain text; document_url fan-out via text.	$0.01 / 1M input	20M	—
DeepSeek-OCR-2	DeepSeek OCR 2 and markdown extraction.	$0.25 / 1M input	3.4B	32K
PaddleOCR-VL 1.6	PaddleOCR-VL-1.6 for OCR, tables, formulas, and charts.	$0.15 / 1M input	0.9B	16K

Benchmarks

Published scores for dots.mocr, from the Infinity-Parser2 technical report.

Evals

ParseBench55.8

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	ParseBench: 55.8.
Cost and performance	Lower listed rate, mid-pack latency

Methods

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

Method	Returns
parse_layout	Layout regions and their text
parse_layout_only	Layout regions
ocr	Lines of text
markdown	The page as Markdown

Estimate cost

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

Pages

$0.90

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/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="rednote-hilab/dots.mocr",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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