Qwen3.5-27B
Parameters
27B
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
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
262,144 tokens
Consumer
7x RTX 4090
24GB VRAM
Datacenter
2x NVIDIA A100
80GB VRAM
Apple Silicon
2x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#100
| Benchmark | Score | Rank |
|---|---|---|
Web Development | 1357 | 90 |
General Text | 1408 | 99 |
Rankings
Overall Rank
#100
Coding Rank
#79
About Qwen3.5-27B
Qwen3.5-27B is Alibaba Cloud's dense multimodal foundation model with 27B parameters, released February 2026. Unlike the MoE variants, it uses a dense architecture combining Gated Delta Networks and Feed Forward Networks. It achieves MMLU-Pro (86.1%), GPQA Diamond (85.5%), SWE-bench Verified (72.4%), and Terminal-Bench 2.0 (41.6%). Features unified vision-language capabilities, 262k native context (extensible to 1M), and excels across reasoning, coding, multimodal understanding, and multilingual tasks spanning 201 languages.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
24
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
5,120
Number of Layers
64
FFN Intermediate Size (Dense)
17,408
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.