Complex Reasoning
Multi-step logic, mathematical proofs, and intricate problem-solving.
Qwen3.5-397B-A17B — Flagship Model for Maximum Reasoning
Qwen3.5-397B-A17B is the flagship Qwen 3.5 model — maximum reasoning power for the most demanding tasks. Try it free.
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Qwen3.5-397B-A17B is the default model for this page. Flagship large-scale Qwen 3.5 model for demanding reasoning and long-form tasks.
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Flagship
MoE
Starter prompts
Free to use on this site via OpenRouter.
Total Params
397B
Active Params
17B
Context
262K native
License
Apache 2.0
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Overview
The Top of the Qwen 3.5 Lineup
Qwen3.5-397B-A17B is the largest public open-weight release in the Qwen 3.5 line. Its published scores are strong on reasoning, coding, search-agent, and multilingual tasks, while its hosted sibling Qwen3.5-Plus adds a 1M default context window and extra production features.
Current Public Scores
This model posts 87.8 on MMLU-Pro, 88.4 on GPQA, and 83.6 on LiveCodeBench v6.
Massive Expert Pool
397B total parameters provide an enormous knowledge base for expert routing.
Open Weight
Full Apache 2.0 license — download, fine-tune, and deploy without restrictions.
Qwen3.5-397B-A17B vs GPT5.2, Claude 4.5 Opus & Gemini-3 Pro
How the flagship open-weight Qwen3.5 model compares to leading frontier models.
Model
MMLU-Pro
GPQA Diamond
LiveCodeBench v6
SWE-bench Verified
Qwen3.5-397B-A17BThis model
MMLU-Pro
87.8
GPQA Diamond
88.4
LiveCodeBench v6
83.6
SWE-bench Verified
76.4
GPT5.2
MMLU-Pro
87.4
GPQA Diamond
92.4
LiveCodeBench v6
87.7
SWE-bench Verified
80.0
Claude 4.5 Opus
MMLU-Pro
89.5
GPQA Diamond
87.0
LiveCodeBench v6
84.8
SWE-bench Verified
80.9
Gemini-3 Pro
MMLU-Pro
89.8
GPQA Diamond
91.9
LiveCodeBench v6
90.7
SWE-bench Verified
76.2
Scores are from official release pages and model cards; blanks (—) are not reported by the source.
Source: Qwen3.5 official release & Hugging Face model cardsUse Cases
What Qwen3.5-397B-A17B Is Best For
Use the flagship when you need the highest quality output and reasoning depth.
Expert Coding
Architecture design, complex refactoring, and multi-repo reasoning.
Scientific Analysis
Process and reason about research papers, datasets, and technical content.
Strategic Planning
Business analysis, decision frameworks, and comprehensive project planning.
Creative Writing
High-quality long-form creative content with nuance and depth.
Benchmark Tasks
When you need the absolute best score on evaluation benchmarks.
FAQ
Qwen3.5-397B-A17B FAQ
Common questions about the flagship model.
Is 397B the best Qwen 3.5 model?
It is the largest public open-weight Qwen3.5 release and posts the strongest published benchmark scores among the open model cards on this site. Smaller models can still be faster, cheaper, or easier to operate.
Can I run it locally?
It requires significant infrastructure — multiple high-end GPUs with large VRAM. Most users access it through cloud APIs or services like OpenRouter.
How does it compare to closed-source models?
Qwen's published benchmark table places it alongside frontier closed-source models across reasoning, agent, and multilingual evaluations. The exact gap still depends on which benchmark and workflow you care about.
When should I use Plus instead?
Qwen3.5-Plus offers strong all-around performance with simpler deployment. Use 397B when you specifically need the highest reasoning ceiling.
How much VRAM does 397B-A17B need?
The full model requires 200+ GB in BF16. Quantized versions can run on 4-8 GPU setups. Cloud deployment via vLLM is the most common approach.
Is 397B-A17B the same as Qwen3.5-Plus?
Qwen3.5-Plus is the hosted version built on 397B-A17B. Plus adds production tooling and a 1M context window. The 397B-A17B weights are the open-weight version you can download.
When is 397B-A17B overkill?
For simple Q&A, drafting, or template generation, smaller models like 9B or 27B are faster and cheaper. Use 397B-A17B when you need the deepest reasoning or best code quality.
Does 397B-A17B support tool calling?
Yes. All Qwen 3.5 models support function calling. The 397B-A17B checkpoint is especially strong when tool use depends on long multi-step reasoning.