Qwen3.6-27B：27B 稠密模型实现旗舰级编程能力

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

AI 导读

Qwen 发布 Qwen3.6-27B 开源模型，这款 27B 参数的稠密模型在编码基准测试中超越了上一代 397B 总参数/17B 激活参数的 MoE 旗舰模型 Qwen3.5-397B-A17B。模型体积从 807GB 大幅缩减至 55.6GB，16.8GB 的量化版本即可在本地运行，生成速度约 25 tokens/s。实测显示其能生成复杂的 SVG 图形代码，展现出旗舰级的编程能力。

推荐理由

通义千问 3.6 27B 用 55GB 瘦身换来了超 397B MoE 的编码能力，Simon 亲手跑出的 SVG 效果惊艳，2026 年最值得本地部署的开源小模型。

正文 · 原文

Simon Willison’s Weblog

22nd April 2026 - Link Blog

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model (via) Big claims from Qwen about their latest open weight model:

Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks.

On Hugging Face Qwen3.5-397B-A17B is 807GB, this new Qwen3.6-27B is 55.6GB.

I tried it out with the 16.8GB Unsloth Qwen3.6-27B-GGUF:Q4_K_M quantized version and llama-server using this recipe by benob on Hacker News, after first installing llama-server using brew install llama.cpp:

llama-server \
    -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M \
    --no-mmproj \
    --fit on \
    -np 1 \
    -c 65536 \
    --cache-ram 4096 -ctxcp 2 \
    --jinja \
    --temp 0.6 \
    --top-p 0.95 \
    --top-k 20 \
    --min-p 0.0 \
    --presence-penalty 0.0 \
    --repeat-penalty 1.0 \
    --reasoning on \
    --chat-template-kwargs '{"preserve_thinking": true}'

On first run that saved the ~17GB model to ~/.cache/huggingface/hub/models--unsloth--Qwen3.6-27B-GGUF.

Here's the transcript for "Generate an SVG of a pelican riding a bicycle". This is an outstanding result for a 16.8GB local model:

Performance numbers reported by llama-server:

Reading: 20 tokens, 0.4s, 54.32 tokens/s
Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s

For good measure, here's Generate an SVG of a NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER (run previously with GLM-5.1):

That one took 6,575 tokens, 4min 25s, 24.74 t/s.

22nd April 2026