Qwen 3.5 27B

Overview

Alibaba's dense 27B Qwen 3.5 with a 262K context window and calibrated thinking mode. One of the best quality-to-size trade-offs in the open 25B-30B class.

When to pick this model

• Math, science, and STEM-heavy reasoning
• Long-context analysis at 100K+ tokens
• Single high-end GPU production deployments
• Multilingual technical workloads
• Replacing closed mid-tier APIs

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	16 GB
Q5_K_M	19 GB
Q8_0	29 GB
FP16 (no quantization)	54 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• 262K native context window
• Well-calibrated thinking mode
• Strong math and science reasoning
• Apache 2.0 license

Limitations

• Needs ~16GB VRAM in Q4
• Gemma 3 27B is a close competitor
• Thinking mode adds latency on simple queries

Architecture & training

Architecture: Dense · 27B · Qwen 3.5 · hybrid thinking · 262k native context

Training: Enriched Qwen 3.5 corpus, strong in complex reasoning with long context.

Quick start

ollama run qwen3.5:27b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.