Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model (https://qwen.ai/blog?id=qwen3.6-27b)

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.

Via Hacker News (https://news.ycombinator.com/item?id=47863217)

Tags: ai (https://simonwillison.net/tags/ai), generative-ai (https://simonwillison.net/tags/generative-ai), local-llms (https://simonwillison.net/tags/local-llms), llms (https://simonwillison.net/tags/llms), qwen (https://simonwillison.net/tags/qwen), pelican-riding-a-bicycle (https://simonwillison.net/tags/pelican-riding-a-bicycle), llama-cpp (https://simonwillison.net/tags/llama-cpp), llm-release (https://simonwillison.net/tags/llm-release), ai-in-china (https://simonwillison.net/tags/ai-in-china)