// 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.
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Qwen3.5 Plus is a proprietary hosted model from Alibaba, built on the Qwen3.5-397B-A17B Mixture-of-Experts architecture with 397 billion total parameters and 17 billion active per token. Its headline feature is a 1-million-token native context window — among the largest available via API — making it well suited for processing entire codebases, long documents, or extended multi-turn conversations in a single request. It supports both a deep-thinking mode and an "Auto" mode that adaptively invokes tools like web search and code interpreters. This April 20, 2026 snapshot reflects ongoing improvements to the model since its original February 2026 launch. The Qwen3.5 series demonstrated strong multimodal performance across reasoning, coding, and vision tasks. A solid general-purpose option for developers needing large-context capabilities without migrating to the newer Qwen3.6 line.
ChatQwen3.6 27B
Qwen3.6 27B is a dense 27-billion-parameter multimodal model from Alibaba's Qwen team, purpose-built for agentic coding and repository-level reasoning. It scores 77.2% on SWE-bench Verified and 59.3% on Terminal-Bench 2.0, outperforming the previous-generation Qwen3.5-397B-A17B across all major coding benchmarks despite being far smaller. It natively supports text, image, and video inputs with a 262K-token context window, extendable to 1M tokens. A standout feature is Thinking Preservation, which retains reasoning traces across conversation turns — reducing redundant computation in multi-step agent loops. The model uses a hybrid attention architecture combining Gated DeltaNet with traditional self-attention. Ideal for developers building coding agents, multi-turn tool-use workflows, or frontend generation pipelines.
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