Qwen/Qwen3.5-397B-A17B

Qwen3.5-397B-A17B

> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

> [!Tip] > For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Alibaba Cloud Model Studio. > > In particular, Qwen3.5-Plus is the hosted version corresponding to Qwen3.5-397B-A17B with more production features, e.g., 1M context length by default, official built-in tools, and adaptive tool use. > For more information, please refer to the User Guide.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

• Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

• Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

• Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

!Benchmark Results

For more details, please refer to our blog post Qwen3.5.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
• Number of Parameters: 397B in total and 17B activated
• Hidden Dimension: 4096
• Token Embedding: 248320 (Padded)
• Number of Layers: 60
• Hidden Layout: 15 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE))
• Gated DeltaNet:
• Number of Linear Attention Heads: 64 for V and 16 for QK
• Head Dimension: 128
• Gated Attention:
• Number of Attention Heads: 32 for Q and 2 for KV
• Head Dimension: 256
• Rotary Position Embedding Dimension: 64
• Mixture Of Experts
• Number of Experts: 512
• Number of Activated Experts: 10 Routed + 1 Shared
• Expert Intermediate Dimension: 1024
• LM Output: 248320 (Padded)
• MTP: trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

GPT5.2 Claude 4.5 Opus Gemini-3 Pro Qwen3-Max-Thinking K2.5-1T-A32B Qwen3.5-397B-A17B

Knowledge

MMLU-Pro 87.4 89.5 89.8 85.7 87.1 87.8

MMLU-Redux 95.0 95.6 95.9 92.8 94.5 94.9

SuperGPQA 67.9 70.6 74.0 67.3 69.2 70.4

C-Eval 90.5 92.2 93.4 93.7 94.0 93.0

Instruction Following

IFEval 94.8 90.9 93.5 93.4 93.9 92.6

IFBench 75.4 58.0 70.4 70.9 70.2 76.5

MultiChallenge 57.9 54.2 64.2 63.3 62.7 67.6

Long Context

AA-LCR 72.7 74.0 70.7 68.7 70.0 68.7

LongBench v2 54.5 64.4 68.2 60.6 61.0 63.2

STEM

GPQA 92.4 87.0 91.9 87.4 87.6 88.4

HLE 35.5 30.8 37.5 30.2 30.1 28.7

HLE-Verified¹ 43.3 38.8 48 37.6 -- 37.6

Reasoning

LiveCodeBench v6 87.7 84.8 90.7 85.9 85.0 83.6

HMMT Feb 25 99.4 92.9 97.3 98.0 95.4 94.8

HMMT Nov 25 100 93.3 93.3 94.7 91.1 92.7

IMOAnswerBench 86.3 84.0 83.3 83.9 81.8 80.9

AIME26 96.7 93.3 90.6 93.3 93.3 91.3

General Agent

BFCL-V4 63.1 77.5 72.5 67.7 68.3 72.9

TAU2-Bench <td style="padding:7px 12px;text-align:center;bord