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Qwen: Qwen3-VL 30B-A3B

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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

 

Code Example

Add AI to your app with the Puter.js AI API, no API keys or setup required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms").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").then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

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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.

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Qwen3.5 Plus 2026-04-20

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.

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Qwen3.6 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

How do I use Qwen3-VL 30B-A3B?

Qwen3-VL 30B-A3B is no longer available through Puter.js. Explore other AI models for alternatives.

What is the pricing for Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B costs $0.2 per 1M input tokens and $0.8 per 1M output tokens.
Price per 1M tokens
Input$0.2
Output$0.8
Who created Qwen3-VL 30B-A3B?

Qwen3-VL 30B-A3B was created by Qwen and released on Apr 2025.

What is the context window of Qwen3-VL 30B-A3B?

Qwen3-VL 30B-A3B supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.

What is the max output length of Qwen3-VL 30B-A3B?

Qwen3-VL 30B-A3B can generate up to 33K tokens in a single response.

What is the knowledge cutoff of Qwen3-VL 30B-A3B?

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.

What types of input can Qwen3-VL 30B-A3B process?

Qwen3-VL 30B-A3B accepts the following input types: text, image. It produces: text.

Does Qwen3-VL 30B-A3B support tool use (function calling)?

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.

Does it work with React / Vue / Vanilla JS / Node / etc.?

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.

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