--- title: Qwen3.5 397B A17B — Multimodal LLM | ModelsLab description: Access Qwen: Qwen3.5 397B A17B API for 397B MoE vision-language tasks. Generate reasoning, code, agents with 17B active params. Try now. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen-qwen35-397b-a17b canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen-qwen35-397b-a17b type: website component: Seo/ModelPage generated_at: 2026-05-13T09:45:11.981093Z --- Available now on ModelsLab · Language Model Qwen: Qwen3.5 397B A17B Frontier MoE Vision LLM --- [Try Qwen: Qwen3.5 397B A17B](/models/alibaba_cloud/qwen-qwen3.5-397b-a17b) [API Documentation](https://docs.modelslab.com) Activate 397B Intelligence Efficiently --- Sparse MoE ### 17B Active Params 397B total parameters activate 17B per pass via 512-expert MoE routing. Native Multimodal ### Vision-Language Fusion Handles text, images, videos with early fusion training across 201 languages. Ultra-Efficient ### 8.6x Faster Decoding Gated Delta Networks deliver 8.6x-19x speed over Qwen3-Max at 262k context. Examples See what Qwen: Qwen3.5 397B A17B can create --- Copy any prompt below and try it yourself in the [playground](/models/alibaba_cloud/qwen-qwen3.5-397b-a17b). Code Review “Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)” Tech Summary “Summarize key specs of Qwen3.5-397B-A17B architecture, including parameter count, context length, and multimodal capabilities.” Agent Plan “Plan steps to deploy a vLLM server for Qwen: Qwen3.5 397B A17B model with tensor-parallel-size 8 and 262k max length.” Reasoning Chain “Solve: A train leaves at 60 mph, another at 70 mph from stations 200 miles apart. When do they meet? Use step-by-step reasoning.” For Developers A few lines of code. Inference. Three lines. --- ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed. - **Serverless:** scales to zero, scales to millions - **Pay per token,** no minimums - **Python and JavaScript SDKs,** plus REST API [API Documentation ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

``` FAQ Common questions about Qwen: Qwen3.5 397B A17B --- [Read the docs ](https://docs.modelslab.com) ### What is Qwen: Qwen3.5 397B A17B model? Open-weight vision-language LLM with 397B total params, 17B active via sparse MoE. Supports 262k native context, extensible to 1M. Competes with GPT-5.2 on reasoning, coding, agents. ### How to use Qwen: Qwen3.5 397B A17B API? Call via standard LLM endpoints with multimodal inputs. Use vLLM: vllm serve Qwen/Qwen3.5-397B-A17B --tensor-parallel-size 8. Enables tool use and 1M context in hosted versions. ### What makes Qwen: Qwen3.5 397B A17B efficient? Hybrid Gated DeltaNet + MoE activates 17B params per pass. Achieves 8.6x faster decoding than Qwen3-Max. Lower compute than 1T peers while ranking #3 open-weights. ### Does Qwen qwen3 5 397b a17b api support vision? Yes, native image/video input via early fusion. First Qwen open model unifying text and vision. Outperforms prior Qwen3-VL on visual benchmarks. ### Is Qwen: Qwen3.5 397B A17B alternative to closed models? Matches frontier like Claude 4.5, Gemini-3 Pro on intelligence index. Open-weights under Apache 2.0 with 201 languages. Smaller active params than Kimi K2.5 or GLM-5. ### Qwen qwen3 5 397b a17b model context length? 262k tokens native, up to 1M extensible via YaRN. Supports reasoning/non-reasoning modes in one model. Ready to create? --- Start generating with Qwen: Qwen3.5 397B A17B on ModelsLab. [Try Qwen: Qwen3.5 397B A17B](/models/alibaba_cloud/qwen-qwen3.5-397b-a17b) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-05-13*