VLM Run

OCR Auto

About

OCR Auto is an open-weight model from VLM Run. Routes each method to its best OCR engine: ocr to PP-OCRv6, markdown to a fast CPU parser (dots.mocr on error), layout to dots.mocr. It accepts text, images, and documents.

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	—
Output / 1M	—

Compare

OCR Auto 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
OCR Auto	Routes each method to its best OCR engine: ocr to PP-OCRv6, markdown to a fast CPU parser (dots.mocr on error), layout to dots.mocr.	—	—	—
DeepSeek-OCR-2	DeepSeek OCR 2 and markdown extraction.	$0.25 / 1M input	3.4B	32K
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	32K
GLM-OCR	Compact multilingual document OCR and markdown extraction.	$0.10 / 1M input	0.9B	8K

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

Methods

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

Method	Returns
markdown	The page as Markdown
ocr	Lines of text
layout	Layout regions

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="vlm-run/ocr:auto",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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