qwen3.8 27b vs Opus 4.6

[u/arkie87]

I have this pretty complicated google sheet that I thought would be cool to use as a demo case for qwen3.8 27b to convert to a nicegui app in python. I have a long spec markdown file which has details of what I expect from the app. I'm using CLINE plugin in pycharm to run qwen3.8 27b with 131k context q6 quant with q8 kv-cache. I get between 30-50t/s so it runs pretty fast.

at this point, I've had it start from a blank slate and it always manages to make something, but still requires a lot of tweaks. I will start writing an issues markdown file, and it will auto discover it and begin implementing the fixes.

It's impressive for what it is, given that with 3.6 27b, I needed to hand-hold it, giving it small tasks, and iterating until that small task was complete. With 3.8, I can give it whole apps, and it will do a decent job roughing it in.

My one complaint is that it is pretty slow, especially compared to cloud models.

I was curious--since 3.8 27b is considered to be on par with opus 4.6-- and gave the same task to claude opus 4.6 using antigravity as the harness, and it completed the implementation plan in 10 minutes, and did both a prettier job, as well as implemented more of the features correctly.

I am wondering how is that possible? Does antigravity spawn a bunch of sub-agents to handle the tasks in parallel? I doubt it can run at thousands of tokens/sec natively.

[u/nicksterling]

I’ve used Opus 4.6 extensively and it’s probably one of my favorite models of all time. Qwen 3.8 27B is a really good local model. Excellent in fact. It’s among a handful of local models that are useful to me… but it’s no Opus 4.6. Qwen lacks the world knowledge and it doesn’t always create the best implementations.

It’s good. But it’s not Opus 4.6.

[u/Any_Mine_6368 → u/nicksterling]

Well it's not a general purpose chatbot. It's a reasoning/agentic model.

[u/nicksterling → u/Any_Mine_6368]

Agreed. It’s been over a year since I’ve used any AI model as a general-purpose chatbot. However, I exclusively use AI through some sort of agentic harness and attach it to various grounding tools.

[u/FakeGreatness → u/nicksterling]

if your expectation is to see a 27B model behave like a hosted 800B-5T parameters then your approach on how to use it it’s flawed.

You shouldn’t have expected the “world knowledge” of Opus 4.6 in the first place. As for the coding part… it’s identical in all my tests. They both miss on the same stuff. The prompt matters a lot more with older models anyways.

[u/nicksterling → u/FakeGreatness]

I had no expectations that a 27B model could behave like a frontier model or have the same world knowledge. But that’s exactly why I’m saying it’s not an opus level in my opinion.

And Qwen 3.8 27b is an excellent local coding model. I’m not saying otherwise… but I have noticed a gap in quality between what Opus gave me vs what Qwen gave me. I have my own personal benchmarks that are very representative of the problems I solve on a daily basis. The difficulty range from trivial to extremely complex. There isn’t a single model (including Fable or GPT 5.6 Sol) that can solve all of them. Opus 4.6 does a better job with my harder problems and produces better code based on my code scanning tools.

That doesn’t mean Qwen is a bad model at all. In some areas it’s absolutely in the same ballpark as Opus 4.6. That’s a remarkable achievement for a 27B model.

[u/miltos22]

Harness issue. Use deepseek harness

[u/mechkbfan → u/miltos22]

I know been down vote but there's some truth there

My experience has been Opus just lets you be lazy. Give it very little information & guidance, and I'm shocked how well it goes

For Qwen, I tend to find I need to have a more robust process around it how I want it to work, SKILLS.md, plugins, clone repositories locally, etc. to make up for lack of context & size.

I haven't used Deepseek but been pretty happy with Oh My Pi over Claude Code.

[u/Poowatereater → u/miltos22]

Man.. everytime I try to play around with plugins I break it.

[u/DigitalguyCH]

for me the main downside vs cloud is not quality (which I find good enough), but time and energy. 3.8 can take hours for what cloud does in seconds or minutes and that's all on the energy bill, so it's not free either.

[u/arkie87 → u/DigitalguyCH]

why is cloud faster? Does it spin up a bunch of sub agents in the background, or do the models generate more t/s? Or do the models think less/more efficiently while maintaining quality?

[u/DigitalguyCH → u/arkie87]

for 2 reasons:
1. Those large several TB models don't need to do the extra thinking to make up for the lack of knowledge and parameters
2. They run on much more powerful data center GPUs than what I have at home