Qwen: Qwen3-VL 30B-A3B
qwen/qwen3-vl-30b-a3b
Access Qwen3-VL 30B-A3B from Qwen 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: "qwen/qwen3-vl-30b-a3b"
}).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/qwen3-vl-30b-a3b"
}).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/qwen3-vl-30b-a3b",
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/qwen3-vl-30b-a3b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Qwen3-VL 30B-A3B is a compact mixture-of-experts vision-language model from Alibaba's Qwen team, with 30B total parameters and only 3B active per token for efficient inference.
It supports image and text inputs with a 131K context window and delivers strong multimodal performance on benchmarks including MMMU and visual-math evaluations. Capabilities include document and chart understanding, OCR, visual coding (generating HTML/CSS/JS from images), 2D spatial grounding, and GUI agent tasks across desktop and mobile interfaces.
The MoE architecture gives it the knowledge breadth of a much larger model while matching the latency and cost profile of a 3B dense model — making it a practical choice for developers who need reliable vision-language capabilities without the compute cost of the 235B flagship variant. Supports tool calling.
Context Window 131K
tokens
Max Output 33K
tokens
Input Cost $0.2
per million tokens
Output Cost $0.8
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Apr 2025
Release Date Apr 2025
Model Playground
Try Qwen3-VL 30B-A3B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
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ChatQwen3.7 Flash
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Frequently Asked Questions
You can access Qwen3-VL 30B-A3B 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 Qwen3-VL 30B-A3B 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 | $0.2 |
| Output | $0.8 |
Qwen3-VL 30B-A3B was created by Qwen and released on Apr 2025.
Qwen3-VL 30B-A3B supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Qwen3-VL 30B-A3B can generate up to 33K tokens in a single response.
Qwen3-VL 30B-A3B has a knowledge cutoff date of Apr 2025. This means the model was trained on data available up to that date.
Qwen3-VL 30B-A3B accepts the following input types: text, image. It produces: text.
Yes, Qwen3-VL 30B-A3B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Qwen3-VL 30B-A3B 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 Qwen3-VL 30B-A3B to your app without worrying about API keys or setup.
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