Qwen3.5-9B
Parameters
9B
Context Length
262K
Modality
Multimodal
Architecture
Dense
License
Apache 2.0
Release Date
24 Feb 2026
Knowledge Cutoff
-
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
262,144 tokens
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Qwen3.5-9B available.
Rankings
Overall Rank
-
Coding Rank
-
About Qwen3.5-9B
Qwen3.5-9B is Alibaba Cloud's efficient multimodal foundation model with 9B parameters, released February 2026. It uses a hybrid architecture combining Gated Delta Networks and Gated Attention in an 8×(3×DeltaNet→FFN→1×Attention→FFN) pattern. It achieves strong scores on MMLU-Pro (82.5%), GPQA Diamond (81.7%), HMMT benchmarks (90%/90%), and LiveCodeBench v6 (82.7%). Features unified vision-language capabilities, 262k native context (extensible to 1M), multi-token prediction training, and excels in multimodal reasoning, coding, agents, and multilingual tasks across 201 languages.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
16
Key-Value Heads
4
Attention Head Dimension
256
Position Embedding
ROPE
RoPE Theta
10,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
Yes
Linear Attention Ratio
75.0%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
12,288
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
248,320
About Qwen 3.5
Qwen 3.5 is Alibaba Cloud's latest-generation foundation model family, released February 2026. It represents a significant leap forward, integrating breakthroughs in multimodal learning (unified vision-language foundation), efficient hybrid architecture (Gated Delta Networks with sparse Mixture-of-Experts), scalable reinforcement learning across million-agent environments, and global linguistic coverage spanning 201 languages. Available under Apache 2.0 license with open weights.