Cached at:
04/22/26, 07:04 PM
# Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
Source: [https://simonwillison.net/2026/Apr/22/qwen36-27b/](https://simonwillison.net/2026/Apr/22/qwen36-27b/)
22nd April 2026 \- Link Blog
**[Qwen3\.6\-27B: Flagship\-Level Coding in a 27B Dense Model](https://qwen.ai/blog?id=qwen3.6-27b)**\([via](https://news.ycombinator.com/item?id=47863217)\) 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](https://huggingface.co/Qwen/Qwen3.5-397B-A17B/tree/main)is 807GB, this new[Qwen3\.6\-27B](https://huggingface.co/Qwen/Qwen3.6-27B/tree/main)is 55\.6GB\.
I tried it out with the 16\.8GB Unsloth[Qwen3\.6\-27B\-GGUF:Q4\_K\_M](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)quantized version and`llama\-server`using this recipe by[benob on Hacker News](https://news.ycombinator.com/item?id=47863217#47865140), 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](https://gist.github.com/simonw/4d99d730c840df594096366db1d27281)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](https://gist.github.com/simonw/95735fe5e76e6fdf1753e6dcce360699)\(run previously[with GLM\-5\.1](https://simonwillison.net/2026/Apr/7/glm-51/)\):
That one took 6,575 tokens, 4min 25s, 24\.74 t/s\.