Qwen: Qwen3.5-2B
qwen/qwen3.5-2b
Try Qwen3.5-2B for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "qwen/qwen3.5-2b"
}).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/qwen3.5-2b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Qwen3.5 2B is a 2-billion parameter multimodal model from Alibaba's Qwen team, released in March 2026 under the Apache 2.0 license as part of the Qwen3.5 Small series with 0.8B, 4B, and 9B siblings. It accepts text, image, and video input and has a native 262,144 token context window.
Qwen's model card reports 66.5 on MMLU-Pro and 51.6 on GPQA with thinking enabled, and 55.3 on MMLU-Pro without it. On vision tasks it lists 64.2 on MMMU, 76.7 on MathVista, and 84.5 on OCRBench. The card shows it scoring above Qwen3-VL-2B on those vision benchmarks, and below Qwen3-4B-2507 on most language benchmarks.
Thinking mode is off by default and is turned on with the enable_thinking parameter. Qwen suggests the model for prototyping and task-specific fine-tuning, which suits developers who need a low-cost model for lightweight text and image tasks.
Context Window 262K
tokens
Max Output N/A
tokens
Input Cost $0.06
per million tokens
Output Cost $0.18
per million tokens
Release Date Mar 2, 2026
Try Qwen3.5-2B for free
Try Qwen3.5-2B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Qwen3.5-2B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 45.6% |
| Humanity's Last Exam Cross-domain reasoning | 2.6% |
| IFBench Instruction following | 31.5% |
| LCR Long-context reasoning | 21.0% |
| Terminal-Bench Hard Agentic terminal tasks | 3.8% |
| τ²-Bench Tool use / agents | 69.0% |
Scores sourced from Artificial Analysis.
Find other Qwen models →
Qwen3.6 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.
ChatQwen3.5 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.
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 Qwen3.5-2B 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.
Qwen3.5-2B is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
Qwen3.5-2B is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.06 |
| Output | $0.18 |
Qwen3.5-2B was created by Qwen and released on Mar 2, 2026.
Qwen3.5-2B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Qwen3.5-2B scores 6.9 on the Artificial Analysis Intelligence Index, outperforming 21% of tracked models. On coding, it scores 2.9 (outperforms 1% of models).
Yes — the Qwen3.5-2B 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.
Add Qwen3.5-2B to your app for free
Developers can integrate Qwen3.5-2B for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.