Qwen: Qwen3.5-27B

Qwen3.5-27B is a dense model with all 27B parameters active, offering stable performance and easier deployment compared to the MoE variants in the Qwen3.5 medium series.

Overview

Released February 24, 2026, Qwen3.5-27B is the dense alternative in the Qwen3.5 medium lineup. Unlike the MoE models (35B-A3B and 122B-A10B), it uses standard FFN layers with all parameters active during inference. This makes it more tolerant of aggressive quantization and simpler to deploy, while still delivering strong performance—including the best SWE-bench Verified score of the medium series.

Benchmark Highlights

• SWE-bench Verified: 72.4% (best of the medium trio, matches GPT-5-mini)
• Strong coding and software engineering performance
• Competitive across language and vision tasks

When to Use Qwen3.5-27B

Choose Qwen3.5-27B when you need:

• Stable, predictable performance
• Aggressive quantization (4-bit and below)
• Simpler deployment without MoE complexity
• Strong coding and software engineering tasks
• Consumer hardware with 21GB+ RAM/VRAM

Choose Qwen3.5-35B-A3B when you need:

• Faster inference (3B vs 27B active parameters)
• Lower memory bandwidth during inference
• Broader benchmark performance

Choose Qwen3.5-122B-A10B when you need:

• Maximum capability for complex tasks
• Long-horizon agentic workflows

Hardware Requirements

Quantization	VRAM/RAM Required
4-bit (Q4_K_M)	~21GB
8-bit	~30GB
FP16	~55GB

The 27B dense model is more forgiving of quantization than MoE variants, maintaining quality at lower bit depths.

Role in Series

Qwen3.5 medium models (Feb 24, 2026):

1. Qwen3.5-122B-A10B: Maximum capability, server deployment
2. Qwen3.5-35B-A3B: Best efficiency, consumer hardware
3. Qwen3.5-27B: Dense stability, easier quantization (this model)

Links

• HuggingFace
• GitHub
• Qwen3.5 Blog
• Unsloth GGUFs