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

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

#401

Post by dTal »

Zetaphor wrote: post #357
fnordpiglet wrote: post #149

Earlier quoted context omitted.

I find it hard to believe anyone who has ever done business inside China doesn’t know that the structure of Chinese business is built around massive IP theft and repurposing on a state wide systematic level. It’s not a nationalism point, it’s an objective and easily verified truth. Most code is not P99, but companies pay a premium to produce code that is. That’s my point.

I'll ask you the same thing I asked the other guy. How is a an open weights model that I can run on my own hardware without an internet connection going to exfiltrate my trade secrets to the Chinese government?

It's the same user and they already answered you: "If you read I’m talking about their service only models."

But yes this is a non-sequitor. The original question was "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?"

Even if you don't trust Chinese companies, and you want a hosted model, you can always pay a third party to host a Chinese open weight model. And it'll be a lot cheaper than OpenAI.

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

#402

Post by dTal »

fnordpiglet wrote: post #374
Zetaphor wrote: post #355

Earlier quoted context omitted.

How exactly do you propose that a local weights model that I can run without an internet connection is going to exfiltrate my trade secrets to the Chinese government?

If you read I’m talking about their service only models.

Why? No one else was. The discussion was about OpenAI / Anthropic's lack of moat when there are open weights models that are almost as good. You can host them anywhere you like. Pay a US company to do so if you want.

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

#403

Post by ginko »

fnordpiglet wrote: post #84
jameson wrote: post #74

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

For coding often quality at the margin is crucial even at a premium. It’s not the same as cranking out spam emails or HN posts at scale. This is why the marginal difference between your median engineer and your P99 engineer is comp is substantial, while the marginal comp difference between your median pick and packer vs your P99 pick and packer isn’t. I’d also say it keeps the frontier shops competitive while costing…

>but if you’re not a Chinese shop, would you really host your production code development workload on a Chinese hosted provider?

The point of open source models is that you host them locally. I trust neither Chinese nor American providers with this.

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

#404

Post by docheinestages »

swalsh wrote: post #302
docheinestages wrote: post #222

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.

Try running with Open Code. It works quite well.

I had an equally painful experience with Open Code. I don't think the harness is the issue. It's the need for a large context window and slow inference.

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

#405

Post by docheinestages »

mswphd wrote: post #309
docheinestages wrote: post #222

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.

context window for Qwen3.6 models' size increase isn't that bad/large (e.g. you can likely fix max context well within the 48GB), but macbook prompt processing is notoriously slow (At least up through M4. M5 got some speedup but I haven't messed with it). One thing to keep in mind is that you do not need to fully fit the model in memory to run it. For example, I'm able to get acceptable token generation speed (~55 to…

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

#406

Post by sleepyeldrazi »

djyde wrote: post #311
finnjohnsen2 wrote: post #261

Since Gemma 4 came this easter the gap from self hosting models to Claude has decreased sigificantly I think. The gap is still huge it just that local models were extremely non-competitive before easter. So now it seems Qwen 3.6 is another bump up from Gemma 4 which is exciting if it is so. I keep an Opus close ofcourse, because these local models still wander off in the wrong direction and fails. Something Opus almo…

What level of programming tasks can a 27B model handle? Even with Claude, I'm occasionally not satisfied, and I can't imagine how effective a 27B model would be.

I ran 3 prompts (short versions, full version in the repo):

- Implement a numerically stable backward pass for layer normalization from scratch in NumPy.

- Design and implement a high-performance fused softmax + top-k kernel in CUDA (or CUDA-like pseudocode).

- Implement an efficient KV-cache system for autoregressive transformer inference from scratch.

and tested Qwen3.6-27B (IQ4_NL on a 3090) against MiniMax-M2.7 and GLM-5 with kimi k2.6 as the judge (imperfect, i know, it was 2AM). Qwen surpassed minimax and won 2/3 of the implementations again GLM-5 according to kimi k2.6, which still sounds insane to me. The env was a pi-mono with basic tools + a websearch tool pointing to my searxng (i dont think any of the models used it), with a slightly customized shorter system prompt. TurboQuant was at 4bit during all qwen tests. Full results https://github.com/sleepyeldrazi/llm_programming_tests.

I am also periodically testing small models in a https://www.whichai.dev style task to see their designs, and qwen3.6 27B also obliterated (imo) the other ones I tested https://github.com/sleepyeldrazi/llm-design-showcase .

Needless to say those tests are non-exhaustive and have flaws, but the trend from the official benchmarks looks like is being confirmed in my testing. If only it were a little faster on my 3090, we'll see how it performs once a DFlash for it drops.

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

#407

Post by sleepyeldrazi »

zkmon wrote: post #240

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 have been getting good results with IQ4_NL and TurboQuant at 4bits on 24gb (3090). It easily fits 256k with that setup, but it starts slowing down quite a bit after 80-100k. Quality in my testing is also still good:

- Coding task test: https://github.com/sleepyeldrazi/llm_programming_tests/ - Design task test: https://github.com/sleepyeldrazi/llm-design-showcase

Coding was against minimax-m2.7 and glm-5, and the design against other small models

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

#408

Post by dTal »

fnordpiglet wrote: post #198
datadrivenangel wrote: post #173

Earlier quoted context omitted.

The challenge is token speed. I did some local coding yesterday with qwen3.6 35b and getting 10-40 tokens per second means that the wall time is much longer. 20 tokens per second is a bit over a thousand tokens per minute, which is slower than the the experience you get with Claude Code or the opus models. Slower and worse is still useful, but not as good in two important dimensions.

Also benchmark measures are not empirical experience measures and are well gamed. As other commenters have said the actual observed behavior is inferior, so it’s not just speed. It’s ludicrous to believe a small parameter count model will out perform a well made high parameter count model. That’s just magical thinking. We’ve not empirically observed any flattening of the scaling laws, and there’s no reason to believe…

Ooh, car analogy time!

It's kinda like saying a car with a 6L engine will always outperform a car with a 2L engine. There are so many different engineering tradeoffs, so many different things to optimize for, so many different metrics for "performance", that while it's broadly true, it doesn't mean you'll always prefer the 6L car. Maybe you care about running costs! Maybe you'd rather own a smaller car than rent a bigger one. Maybe the 2L car is just better engineered. Maybe you work in food delivery in a dense city and what you actually need is a 50cc moped, because agility and latency are more important than performance at the margins.

And if you're the only game in town, and you only sell 6L behemoths, and some upstart comes along and starts selling nippy little 2L utility vehicles (or worse - giving them away!) you should absolutely be worried about your lunch. Note that this literally happened to the US car industry when Japanese imports started becoming popular in the 80s...

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

#409

Post by pja »

I know this is kind of old hat by now, but it kind of blows my mind that I can upload a hand drawn decision tree & get a transcribed dot file back on consumer hardware using a pile of linear algebra that wasn’t even particularly specialised for this purpose, it’s just a capability that it picked up along with everything else during training.

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

#410

Post by amunozo »

pja wrote: post #409

I know this is kind of old hat by now, but it kind of blows my mind that I can upload a hand drawn decision tree & get a transcribed dot file back on consumer hardware using a pile of linear algebra that wasn’t even particularly specialised for this purpose, it’s just a capability that it picked up along with everything else during training.

I totally agree. The feeling you get by running these things locally is different, as if you could feel the magic closer.
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