Qwen: Qwen3.5 397B A17B
Try Qwen3.5 397B A17B 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/qwen3.5-397b-a17b"
}).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/qwen3.5-397b-a17b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Qwen3.5-397B-A17B is an open-weight native vision-language model from Alibaba's Qwen team, released in February 2026. It uses a hybrid architecture combining Gated Delta Networks (linear attention) with a sparse mixture-of-experts design, totaling 397 billion parameters but activating only 17 billion per forward pass for efficient inference. The model delivers strong performance across reasoning, coding, agent tasks, and multimodal understanding, competing with frontier models like GPT-5.2, Claude 4.5 Opus, and Gemini-3 Pro. It supports 201 languages and dialects and features a 250k-token vocabulary. Its decoding throughput is reported at 8.6x that of Qwen3-Max under a 32k context length.
Context Window 262K
tokens
Max Output 66K
tokens
Input Cost $0.6
per million tokens
Output Cost $3.6
per million tokens
Input text, image, video, audio
modalities
Tool Use Yes
Release Date Feb 15, 2026
Output Speed 88
tokens / sec
Latency 1.79s
time to first token
Try Qwen3.5 397B A17B for free
Try Qwen3.5 397B A17B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Qwen3.5 397B A17B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 89.3% |
| Humanity's Last Exam Cross-domain reasoning | 29.0% |
| SciCode Scientific programming | 44.8% |
| IFBench Instruction following | 78.8% |
| LCR Long-context reasoning | 77.3% |
| Terminal-Bench Hard Agentic terminal tasks | 40.9% |
| τ²-Bench Tool use / agents | 95.6% |
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 397B A17B 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 397B A17B 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 397B A17B 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 | $0.6 |
| Output | $3.6 |
Qwen3.5 397B A17B was created by Qwen and released on Feb 15, 2026.
Qwen3.5 397B A17B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Qwen3.5 397B A17B can generate up to 66K tokens in a single response.
Qwen3.5 397B A17B accepts the following input types: text, image, video, audio. It produces: text.
Yes, Qwen3.5 397B A17B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Qwen3.5 397B A17B scores 18.4 on the Artificial Analysis Intelligence Index, outperforming 64% of tracked models. On coding, it scores 48.2 (outperforms 54% of models).
Yes — the Qwen3.5 397B A17B 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 397B A17B to your app for free
Developers can integrate Qwen3.5 397B A17B for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.