Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

qwen.ai

Post reply on HN
Print view View original

91–100 of 482 posts

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

#91

Post by rohansood15 »

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…

Most code is not P99 though.

Also, have you considered that your trust in Anthropic and distrust in China may not be shared by many outside the US? There's a reason why Huawei is the largest supplier of 5G hardware globally.

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

#92

Post by hedgehog »

muyuu wrote: post #62
Aurornis wrote: post #23

Earlier quoted context omitted.

The 27B model they release directly would require significant hardware to run natively at 16-bit: A Mac or Strix Halo 128GB system, multiple high memory consumer GPUs, or an RTX 6000 workstation card. This is why they don’t advertise which consumer hardware it can run on: Their direct release that delivers these results cannot fit on your average consumer system. Most consumers don’t run the model they release direct…

i have a Strix Halo machine typically those dense models are too slow on Strix Halo to be practical, expect 5-7 tps you can get an idea by looking at other dense benchmarks here: https://strixhalo.zurkowski.net/experiments - i'd expect this model to be tested here soon, i don't think i will personally bother

This one is around 250 t/s prefill and 12.4 generation in my testing.

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

#93

Post by Marciplan »

yrds96 wrote: post #87
sietsietnoac wrote: post #38

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 wonder if this became a so well known "benchmark" that models already got trained for it.

every model release Simon comes with his Pelican and then this comment follows.

Can we stop both? its so boring

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

#94

Post by [deleted] »

Aurornis wrote: post #26
mottosso wrote: post #18

Earlier quoted context omitted.

Specs for whatever they used to achieve the benchmarks would be a good start.

The benchmarks are from the unquantized model they release. This will only run on server hardware, some workstation GPUs, or some 128GB unified memory systems. It’s a situation where if you have to ask, you can’t run the exact model they released. You have to wait for quantizations to smaller sizes, which come in a lot of varieties and have quality tradeoffs.

[deleted]

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

#95

Post by HotHotLava »

yrds96 wrote: post #87
sietsietnoac wrote: post #38

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 wonder if this became a so well known "benchmark" that models already got trained for it.

Given that the pelican looks way better than the dragon, it almost seems like a certainty.

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

#96

Post by runjake »

rohansood15 wrote: post #91
fnordpiglet wrote: post #84

Earlier quoted context omitted.

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…

Most code is not P99 though. Also, have you considered that your trust in Anthropic and distrust in China may not be shared by many outside the US? There's a reason why Huawei is the largest supplier of 5G hardware globally.

You're right, but perspective is important, and that's because China and the US are engaged in economic warfare (even before the current US regime), vying for the dubious title of "superpower".

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

#97

Post by syntaxing »

Aurornis wrote: post #75
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

I use Opus and the Qwen models. The gap between them is much larger than the benchmark charts show. If you want to compare to a hosted model, look toward the GLM hosted model. It’s closest to the big players right now. They were selling it at very low prices but have started raising the price recently.

Yes and no. Are you using open router or local? Are the models are good as Opus? No. But 99% of the time, local models are terrible because of user errors. Especially true for MoE, even though the perplexity only drops minimal for Q4 and q4_0 for the KV cache, the models get noticeably worse.

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

#98

Post by Oras »

Aurornis wrote: post #23
anonzzzies wrote: post #11

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

The 27B model they release directly would require significant hardware to run natively at 16-bit: A Mac or Strix Halo 128GB system, multiple high memory consumer GPUs, or an RTX 6000 workstation card. This is why they don’t advertise which consumer hardware it can run on: Their direct release that delivers these results cannot fit on your average consumer system. Most consumers don’t run the model they release direct…

If these models reach quality of Opus 4.5, then DGX could be a good alternative for serious dev teams to run local models. It is not that expensive and has short time to make ROI

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

#99

Post by bityard »

Aurornis wrote: post #26
mottosso wrote: post #18

Earlier quoted context omitted.

Specs for whatever they used to achieve the benchmarks would be a good start.

The benchmarks are from the unquantized model they release. This will only run on server hardware, some workstation GPUs, or some 128GB unified memory systems. It’s a situation where if you have to ask, you can’t run the exact model they released. You have to wait for quantizations to smaller sizes, which come in a lot of varieties and have quality tradeoffs.

This would likely run fine in just 96 GB of VRAM, by my estimation. Well within the ability of an enthusiastic hobbyist with a few thousand dollars of disposable income.

Quantizations are already out: https://huggingface.co/unsloth/Qwen3.6-27B-GGUF

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

#100

Post by amstan »

GrinningFool wrote: post #77
spwa4 wrote: post #15

Unsloth quants available: https://unsloth.ai/docs/models/qwen3.6

128GB (112 GB avail) Strix AI 395+ Radeon 8060x (gfx1151) llama-* version 8889 w/ rocm support ; nightly rocm llama.cpp/build/bin/llama-batched-bench --version unsloth/Qwen3.6-27B-GGUF:UD-Q8_K_XL -npp 1000,2000,4000,8000,16000,32000 -ntg 128 -npl 1 -c 34000 | PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s | |-------|--------|------|--------|----------|----------|----------|----------|-------…

you should try vulkan instead of rocm. it goes like 20% faster.
Post reply on HN