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.

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.

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?

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.

Qwen-3.8-27B using qwen-code via ollama on a desktop machine with 64G ram and a GeForce RTX 3060 with 12G vram.

I used the owntone-server codebase downloaded from github.

/init

It is unbelievably slow. Ok, sure.

It has hallucinated a macro "DLOG" from its read_file output(!!!) and so tied itself in knots over it because it can't find the macro that is wholly absent from the codebase. Hours later its still going and shows no prospect of writing anything to QWEN.md

How the hell are my results so very different to what you're claiming? What have I done wrong here?

I haven't got anywhere near planning anything with it, much less have it implement something, at any quality. I mean it's just so bad.

Edit: Asked claude which suggested a Modelfile

   FROM qwen3.8:27b
   PARAMETER num_ctx 16384
   PARAMETER num_thread 12

I'll give that a go. I wonder if you guys just have a bunch of H100s or something like this.