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

The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/

I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine.

Performance numbers:

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

On llama server, the Q4_K_M is giving about 91k context on 24GB, which calculates to about 70MB per 1K context (KV-Cache). I could have gone for Q5 which probably leaves about 30K token space. I think this is pretty impressive.

I wish that all announcements of models would show what (consumer) hardware you can run this on today, costs and tok/s.

TIL that our corporate network site blocker classifies qwen.ai as a sex site…

Been using Qwen 3.6 35B and Gemma 4 26B on my M4 MBP, and while it’s no Opus, it does 95% of what I need which is already crazy since everything runs fully local.

What competitive advantage does OpenAI/Anthropic has when companies like Qwen/Minimax/etc are open sourcing models that shows similar (yet below than OpenAI/Anthropic) benchmark results?

Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1]

[1]: https://artificialanalysis.ai/models/#pricing

Generate an SVG of a pelican riding a bicycle: https://codepen.io/chdskndyq11546/pen/yyaWGJx

Generate an SVG of a dragon eating a hotdog while driving a car: https://codepen.io/chdskndyq11546/pen/xbENmgK

Far from perfect, but it really shows how powerful these models can get

I'll be really interested to hear qualitative reports of how this model works out in practice. I just can't believe that a model this small is actually as good as Opus, which is rumored to be about two orders of magnitude larger.

I'm kind of interested in a setup where one buys local hardware specifically to run a crap ton of small-to-medium LLM locally 24/7 at high throughput. These models might now be smart enough to make all kinds of autonomous agent workflows viable at a cheap price, with a good queue prioritization system for queries to fully utilize the hardware.

Has anyone tried using this with a Claude Code or Qwen Code? They both require very large context windows (32k and 16k respectively), which on a Mac M4 48GB serving the model via LM Studio is painfully slow.

Q4-Q5 quants of this model runs well on gaming laptops with 24GB VRAM and 64GB RAM. Can get one of those for around $3,500.

Interesting pros/cons vs the new Macbook Pros depending on your prefs.

And Linux runs better than ever on such machines.

Any comparisons against Qwen3.6-35B-A3B?

This is getting very close to fit a single 3090 with 24gb VRAM :)

I'm experimenting with this on my RTX 3090 and opencode. It is pretty impressive so far.

I have been running the slightly larger 31B model for local coding:

ollama launch claude --model qwen3.6:35b-a3b-nvfp4

This has been optimized for Apple Silicon and runs well on a 32G ram system. Local models are getting better!

Good news!

Friendly reminder: wait a couple weeks to judge the ”final” quality of these free models. Many of them suffer from hidden bugs when connected to an inference backend or bad configs that slow them down. The dev community usually takes a week or two to find the most glaring issues. Some of them may require patches to tools like llama.cpp, and some require users to avoid specific default options.

Gemma 4 had some issues that were ironed out within a week or two. This model is likely no different. Take initial impressions with a grain of salt.

It's a rap on claude

I've been waiting for this one. I've been using 3.5-27b with pretty good success for coding in C,C++ and Verilog. It's definitely helped in the light of less Claude availability on the Pro plan now. If their benchmarks are right then the improvement over 3.5 should mean I'm going to be using Claude even less.

Are there any "optimized" models, that have lesser hardware requirements and are specialised in single programming language, e.g. C# ?

A bit skeptical about a 27B model comparable to opus...

Does anyone know good provider for low latency llm api provider? We tried to look at Cerebras and Groq but they have 0 capacity right now. GPT models are too slow for us at the moment. Gemini are better but not really at same level as GPT.

Has anyone tested it at home yet and wants to share early impressions?

I really like local models for code reviews / security audits.

Even if they don't run super fast, I can let them work overnight and get comprehensive reports in the morning.

I used Qwen3.6-27B on an M5 (oq8, using omlx) and Swival (https://swival.dev) /audit command on small code bases I use for benchmarking models for security audits.

It found 8 out of 10, which is excellent for a local model, produced valid patches, and didn't report any false positives. which is even better.

Excited to try this, the Qwen 3.6 MoE they just released a week or so back had a noticeable performance bump from 3.5 in a rather short period of time.

For anyone invested in running LLMs at home or on a much more modest budget rig for corporate purposes, Gemma 4 and Qwen 3.6 are some of the most promising models available.

How much VRAM is needed?

Unsloth quants available:

https://unsloth.ai/docs/models/qwen3.6

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