Qwen3.5-397B-A17B
Qwen3.5-397B-A17B is Alibaba's flagship open-weight MoE model. It features native multimodal reasoning, a 1M context window, and a 19x decoding throughput...
alibabaQwen3.5February 16, 2026About Qwen3.5-397B-A17B
Learn about Qwen3.5-397B-A17B's capabilities, features, and how it can help you achieve better results.
High-Efficiency Mixture of Experts
Qwen3.5-397B-A17B is a flagship native multimodal model that utilizes an innovative hybrid architecture fusing linear attention through Gated Delta Networks with a sparse Mixture-of-Experts (MoE). Although it contains 397 billion total parameters, its sparse design activates only 17 billion parameters per forward pass, achieving exceptional inference efficiency and speed without compromising on its vast reasoning capabilities. It is optimized for both language and visual tasks, supporting a massive vocabulary of 250k tokens and providing support for over 201 languages and dialects.
Native Multimodal Agentic Workflows
The model excels as a native multimodal agent, capable of processing up to one million tokens of context, which is equivalent to approximately two hours of video. It introduces a specialized Thinking Mode for complex logical reasoning and is natively equipped for agentic workflows, including web development, GUI navigation, and real-world spatial intelligence. Its architecture supports FP8 end-to-end training and a disaggregated training-inference framework, making it one of the most scalable and efficient models for enterprise-grade AI applications.
Open Weights for Global Accessibility
Released under the Apache 2.0 license, this model provides the open-source community with frontier-level capabilities previously restricted to proprietary systems. It bridges the gap between massive parameter counts and practical deployment, allowing organizations to run state-of-the-art reasoning tasks on private infrastructure with significantly lower compute overhead than dense 400B alternatives.
Use Cases
Discover the different ways you can use Qwen3.5-397B-A17B to achieve great results.
Long-Horizon Video Analysis
Analyze up to two hours of video content to extract logic, reverse-engineer code from footage, or generate structured summaries.
PhD-Level STEM Research
Solve graduate-level PhD science questions and olympiad-level math problems using its adaptive deep-thinking mode.
Autonomous GUI Agents
Automate interactions with smartphones and computers to handle office workflows and cross-app mobile navigation.
Visual Software Engineering
Execute 'vibe coding' by turning natural language instructions and UI sketches into functional frontend code.
Document Intelligence
Process complex documents, charts, and handwritten sketches to extract structured data and reverse-engineer layouts.
Spatial AI Applications
Understand pixel-level relationships for embodied AI tasks like autonomous driving scene analysis and robotic navigation.
Strengths
Limitations
API Quick Start
alibaba/qwen3.5-plus
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1',
});
async function main() {
const completion = await client.chat.completions.create({
model: 'qwen3.5-plus',
messages: [{ role: 'user', content: 'Analyze the logic of this MoE architecture.' }],
extra_body: { enable_thinking: true },
});
console.log(completion.choices[0].message.content);
}
main();Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about Qwen3.5-397B-A17B
“Qwen3.5-397B is essentially a GPT-5 class model but open-weight. The DeltaNet architecture is solving the MoE latency issues perfectly.”
“Native multimodal reasoning on Qwen3.5 looks incredible. 1M context + video analysis is going to change agent workflows.”
“The decision to use FP8 end-to-end training while maintaining BF16 in sensitive layers is a masterclass in stability optimizations.”
“This is the first time I've seen an open model actually beat Gemini 1.5 Pro on complex multimodal agent tasks.”
“The 19x decoding throughput improvement over Qwen3-Max makes this a viable alternative for production-level agents.”
“I was surprised by how well it handles 4-bit quantization. It keeps almost all the reasoning capability on a dual A100 setup.”
Related Videos
Watch tutorials, reviews, and discussions about Qwen3.5-397B-A17B
“A 397 billion parameter model, but with 17 billion parameters active.”
“When decoding at 256K, this model is 19 times faster than Qwen 3 Max.”
“The native vision-language reasoning is what sets this apart for agentic workflows.”
“This beats most closed models on the standard math benchmarks.”
“Running this locally is tough, but the quantized versions are workable on high-end macs.”
“397 billion parameter model with 17 billion active parameters. It's natively multimodal.”
“It's probably currently the best open-source multimodal model.”
“The ability to process two hours of video natively is a massive advantage.”
“Look at these logic scores, it's hitting GPT-4o levels consistently.”
“The Apache license makes this very attractive for corporate data privacy.”
“OCR structured extraction. You have a messy PDF... and you need to turn that into clean JSON. This model excels there.”
“You get the intelligence of a 400 billion parameter giant... but you pay the compute cost of a 17 billion parameter model.”
“It handles long-context retrieval better than the previous version.”
“The tool use integration is built into the base training, not an afterthought.”
“Thinking mode allows it to correct its own logic before outputting.”
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Pro Tips
Expert tips to help you get the most out of Qwen3.5-397B-A17B and achieve better results.
Toggle Thinking Mode
Pass the 'enable_thinking: true' parameter in your API call to activate deep reasoning for math, coding, and complex logic puzzles.
Utilize Fast Mode
Use 'Fast' mode for simple queries to get instant answers without consuming tokens on unnecessary internal thinking phases.
Optimize Video Prompts
When analyzing video, prompt the model to focus on the final dynamic outcome rather than frame-by-frame analysis for better temporal coherence.
Leverage Quantization
Use 4-bit or 8-bit quantization (GGUF/EXL2) to run the model on consumer-grade hardware if you have sufficient VRAM (200GB+).
Testimonials
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Frequently Asked Questions
Find answers to common questions about Qwen3.5-397B-A17B