Qwen-3.8-27B - the best thing that happened in AI so far.

This thing can accomplish the same results as the frontier models do, if you are willing to play a role of an higher level architect or decision maker. Guide it a bit here and there like a tech lead and you get everything done.

The money you pay for frontier models is exactly for outsourcing that high level direction work to agents.

> Qwen-3.8-27B - the best thing that happened in AI so far.

Qwen3.8-Flash_Next should be in the running for that title as well. It's astonishingly good. I use both of them. If I need more memory on the machine for other things I run 27B, If I can get by with much less RAM I run QFN (takes about 102GB on my Strix Halo box with 262,144 context). QFN runs quite a bit faster on my hardware - about 50t/s. 27B with MTP starts out around 22t/s but degrades over time as the context fills up. Both are great models, but I'd give QFN a bit of an edge (at least for the coding projects I've worked on). But both feel on par with Claude Opus 4.6 - 4.8.

Can you tell a little more about your software setup? Been playing with local AI models but struggle to get anything as easy as ChatGPT or Claude. I have a folder with skills, and I can switch between ChatGPT and Claude, and either will do the same work. I’m struggling to find something that can do that locally

I feel like Alibaba is stealing from the frontier models, and I’m a bit queasy taking advantage of that. If they aren’t distilling and it’s actually true they have superior efficiency as they claim, then why aren’t they way ahead?

> Guide it a bit here and there like a tech lead and you get everything done

How do you validate and verify that outcome?

Or does Qwen-3.8-27B do the validation and verification for you as well? If so, how do you ensure that's correct?

> This thing can accomplish the same results as the frontier models do

What frontier models are you using? I'm literally sitting with Qwen 3.8 27B locally and trying my damnest to get it to work at least 80% of the quality Astra et al have, and can't get close to it. It keeps being so verbose, regardless of inference parameters, system prompts, reasoning effort, and keeps going on tangents. I'm running the BF16 weights via vLLM, can't imagine how much worse the experience must be with the quantized weights people run with the average consumer GPU.

Even with tight guiding, as I guess you could say I do more "AI pair-programming" than vibecoding, it just loses the plot to often and waste time, enough that it's clearly not an alternative to today's frontier models.

Gemma4:31b is better. Qwen is really good at speed (especially with megakernels), but if its going in a wrong direction, it will go in the wrong direction hard. Gemma can self correct with its thinking.

The only reason Qwen is more popular is because it can run decently fast on MLX, while Gemma needs a discrete GPU with 24gb of ram to run at usable speeds.

Also, theoretically speaking, any small model can be as good as a big model if all they have to do is summarize text. Back before agents were a thing, we had prototype coders running of local inference models from Mistral for ai dev. The workflow was to generate llm summaries of the codebase, and a summary of changes to be made. Then we would grab search results from the web, feed it to an llm to summarize, and then finally combine all of that context into a final inference run which would generate code, then the codebase would be run automatically, any errors would be llm analyzed, and an llm would generate the next change to be made in the above cycle.

Nowdays, we can do that with pi coding agent and Gemma4:31b. But we still have plenty of custom harnesses for things.