PaddlePaddle

PP-OCRv6

About

PP-OCRv6 is a 20M open-weight model from PaddlePaddle. PaddleOCR PP-OCRv6 medium text detection and recognition; scene OCR JSONL on image chat; text returns plain text; document_url fan-out via text. It accepts images and documents. License is Apache-2.0. Released June 11, 2026.

Parameters
20M
Published parameter count.
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	$0.01
Cached input / 1M	$0.01
Output / 1M	$0.20

Compare

PP-OCRv6 is the smallest of these OCR models, at 20M, and its published input rate is the lowest, $0.01 / 1M input.

Model	What it does	Price	Parameters	Context
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	—
PaddleOCR-VL 1.6	PaddleOCR-VL-1.6 for OCR, tables, formulas, and charts.	$0.15 / 1M input	0.9B	16K
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

Benchmarks

Published scores for PP-OCRv6, from the PP-OCRv6 paper. Medium tier.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	Detection Hmean: 86.2.
Cost and performance	Lower listed rate, mid-pack latency

Examples

Scene-text OCR
PP-OCRv6 reads a New York Times newsstand in 120 ms.
{"lines":[{"text":"The New York Times","score":0.99,"box_xyxy":[..]}, ...]}

Methods

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

Method	Returns
ocr	Lines of text
detect	Boxes around objects
text	Plain text

Estimate cost

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

Pages

$0.23

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="paddleocr/pp-ocrv6",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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