Baidu: Qianfan OCR Fast
baidu/qianfan-ocr-fast:free
Access Qianfan OCR Fast from Baidu using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "baidu/qianfan-ocr-fast:free"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "baidu/qianfan-ocr-fast:free"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="baidu/qianfan-ocr-fast:free",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "baidu/qianfan-ocr-fast:free",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Qianfan OCR Fast is a document intelligence model from Baidu's Qianfan team, purpose-built for optical character recognition tasks. It is an upgraded variant of the base Qianfan-OCR, trained on specialized OCR data while retaining general multimodal capabilities.
The underlying Qianfan-OCR architecture is a 4B-parameter end-to-end vision-language model that replaces traditional multi-stage OCR pipelines with a single model handling document parsing, layout analysis, table extraction, chart understanding, key information extraction, and document QA. It performs direct image-to-Markdown conversion and supports 192 languages. The base model scored 93.12 on OmniDocBench v1.5 and 79.8 on OlmOCR Bench, leading all end-to-end models on both.
Qianfan OCR Fast offers a 65K-token context window and is well suited for developers building document processing pipelines — invoice parsing, report extraction, exam grading, or RAG over scanned documents.
Context Window 66K
tokens
Max Output 29K
tokens
Input Cost $0
per million tokens
Output Cost $0
per million tokens
Release Date Apr 20, 2026
Model Playground
Try Qianfan OCR Fast instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
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Frequently Asked Questions
You can access Qianfan OCR Fast by Baidu through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Qianfan OCR Fast to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0 |
| Output | $0 |
Qianfan OCR Fast was created by Baidu and released on Apr 20, 2026.
Qianfan OCR Fast supports a context window of 66K tokens. For reference, that is roughly equivalent to 131 pages of text.
Qianfan OCR Fast can generate up to 29K tokens in a single response.
Yes — the Qianfan OCR Fast API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Qianfan OCR Fast to your app without worrying about API keys or setup.
Read the Docs View Tutorials