PaddlePaddle

PaddleOCR-VL 1.6

About

PaddleOCR-VL 1.6 is a 0.9B open-weight model from PaddlePaddle. PaddleOCR-VL-1.6 for OCR, tables, formulas, and charts. It accepts text, images, and documents. Context window is 16K. License is Apache-2.0. Released May 27, 2026.

Parameters
0.9B
Published parameter count.
Context window
16K
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	$0.15
Cached input / 1M	$0.15
Output / 1M	$0.35

Compare

PaddleOCR-VL 1.6 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
PaddleOCR-VL 1.6	PaddleOCR-VL-1.6 for OCR, tables, formulas, and charts.	$0.15 / 1M input	0.9B	16K
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
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	32K

Benchmarks

Published scores for PaddleOCR-VL 1.6, from the OmniDocBench leaderboard.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	OmniDocBench v1.6: 96.34.
Cost and performance	Lower listed rate, mid-pack latency

Methods

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

Method	Returns
ocr	Lines of text
table	Tables as Markdown
formula	Formulas as Markdown
chart	Charts as Markdown

Estimate cost

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

Pages

$0.73

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="paddlepaddle/paddleocr-vl-1.6",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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