Qwen3.5 397B A17B is Alibaba's large sparse Mixture-of-Experts model with 397 billion total parameters and 17 billion activated per token, built for frontier-class reasoning and coding. It is multimodal across text, image, and video, with a 262,144 token native context window extensible toward roughly one million tokens with YaRN scaling. Reported scores include MMLU-Pro 87.8, MMMU-Pro 79.0, and SWE-bench Verified 76.4. The model runs in thinking mode by default, generating reasoning tokens before its response, and supports recommended output lengths up to 81,920 tokens for hard problems. It targets demanding agentic workflows, repository-level code reasoning, structured outputs, and tool calling across more than 200 languages.
Qwen3.5 397B A17B runs on 3 different routes through the Opper gateway. Compare residency, ZDR, and training posture at a glance β full data-handling detail per route below.
Training posture across routes: No training on prompts by default.
Each route hosting Qwen3.5 397B A17B has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; unknown.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; SCCs; DPA available.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; SCCs; DPA available.
Independent benchmark scores β composite indices for reasoning, coding, and math, plus individual eval scores where available.
Call Qwen3.5 397B A17B through the Opper gateway with one API key. Let your coding agent set it up, or call it directly β Opper is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into your coding agent β Claude Code, Cursor, Codex, and more β and it'll wire up Opper for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.OPPER_API_KEY,baseURL: "https://api.opper.ai/v3/compat",});const completion = await client.chat.completions.create({model: "deepinfra/Qwen/Qwen3.5-397B-A17B",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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