// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "qwen/qwen-vl-ocr"
}).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: "qwen/qwen-vl-ocr"
}).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="qwen/qwen-vl-ocr",
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": "qwen/qwen-vl-ocr",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Qwen-VL OCR is Alibaba's specialized vision-language model purpose-built for text extraction and document parsing, derived from the Qwen-VL series.
Unlike general-purpose VL models, it's optimized for OCR across scanned documents, tables, receipts, exam papers, forms, and handwritten content. It supports multilingual recognition including English, Chinese, French, German, Japanese, Korean, Russian, Italian, and Arabic.
Capabilities include skewed image recognition, text localization with bounding box coordinates, table-to-HTML parsing, document-to-LaTeX conversion, and formula transcription. Built-in task modes return structured output as plain text, JSON, HTML, or LaTeX depending on the workflow.
It's the right Qwen API choice for developers building document digitization, receipt parsing, or information extraction pipelines that need OCR-focused accuracy rather than general visual reasoning.
Context Window 34K
tokens
Max Output 4K
tokens
Input Cost $0.72
per million tokens
Output Cost $0.72
per million tokens
Input text, image
modalities
Tool Use No
Knowledge Cutoff Apr 2024
Release Date Oct 28, 2024
Model Playground
Try Qwen-VL OCR instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Qwen
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Qwen3.7 Plus is Alibaba's multimodal agent model, released in June 2026, combining vision-language understanding with full agentic capabilities across a 1 million-token context window. Unlike the text-only Qwen3.7 Max, Plus ingests images and video alongside text, processed through early-fusion training so vision and language are jointly understood from the first layer. This enables GUI grounding — the model can interpret screenshots and issue precise on-screen actions — scoring 79.0 on ScreenSpot Pro, placing it alongside Claude Computer Use and OpenAI Operator in the GUI automation tier. Beyond vision, it adds deep reasoning, self-programming, tool invocation, and autonomous iteration: the model writes and tests code, calls external APIs, and loops until the task is done. On the Artificial Analysis Intelligence Index it scores 53. Choose it over Qwen3.7 Max when your workflow requires image or video inputs, browser/desktop automation, or end-to-end agentic pipelines that combine seeing, reasoning, and doing.
ChatQwen3.7 Max
Qwen3.7 Max is Alibaba's flagship proprietary reasoning model, released in May 2026, built for long-horizon agentic workloads with a 1 million-token context window and a chain-of-thought reasoning architecture. It is purpose-built for complex, multi-step autonomous tasks. Alibaba demonstrated the model running for 35 hours without degradation, executing over 1,000 tool calls in a single session — making it a strong candidate for coding agents, automated pipelines, and deep document analysis. On benchmarks, it ranks 13th globally on LM Arena's text leaderboard and scores 56.6 on the Artificial Analysis Intelligence Index, making it the highest-ranked Chinese model on that index. It posted 90.2 on Arena-Hard v2 and 72.5 on SWE-Bench Verified. Qwen3.7 Max supports the Anthropic API protocol natively, so it integrates cleanly with tooling like Claude Code. It is well-suited for developers building coding assistants, research agents, or any API use case requiring extended reasoning over large contexts.
ChatQwen3.6 Flash
Qwen3.6 Flash is the speed-optimized tier of Alibaba's Qwen3.6 model family, designed for high-throughput, low-latency inference pipelines. It sits alongside Qwen3.6 Max Preview, Plus, and 35B-A3B in the product lineup, targeting use cases where fast response times matter more than peak benchmark scores. Like other Qwen3.6 models, it builds on a hybrid architecture combining linear attention with sparse mixture-of-experts routing. It is best suited for high-volume production workloads such as classification, extraction, summarization, and lightweight agent tasks where latency and cost efficiency are the primary constraints.
Frequently Asked Questions
You can access Qwen-VL OCR by Qwen 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 Qwen-VL OCR 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 | $0.72 |
| Output | $0.72 |
Qwen-VL OCR was created by Qwen and released on Oct 28, 2024.
Qwen-VL OCR supports a context window of 34K tokens. For reference, that is roughly equivalent to 68 pages of text.
Qwen-VL OCR can generate up to 4K tokens in a single response.
Qwen-VL OCR has a knowledge cutoff date of Apr 2024. This means the model was trained on data available up to that date.
Qwen-VL OCR accepts the following input types: text, image. It produces: text.
No, Qwen-VL OCR does not currently support tool use (function calling).
Yes — the Qwen-VL OCR 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 Qwen-VL OCR to your app without worrying about API keys or setup.
Read the Docs View Tutorials