I have an internal automated benchmark, which roughly follows my workflow, and I've been testing various models on it, local and cloud. Qwen 3.8 27B did awesome. Its understanding is correct, research is better than e.g. glm's (and I like glm), and implementation is good and careful.

Qwen 3.8 27B doesn't look benchmaxxed. These "52 AA score" numbers feel real, which is surprising. I've been using it locally for a few days for other tasks as well. If not the speed, I'd be totally happy to use it as a daily driver instead of cloud models, it is that good.

--- (benchmark, to get an idea):

1. First, initial prompt which is not super precise - similar to how I'd write a task when talking e.g. to Opus. I'm describing an idea, and asking model to come up with some plan, and also to criticize the approach. Task is about implementing a particular pi extension. I'm checking if a model actually understands what I'm asking.

2. Then, as a follow-up, I ask to research alternative implementations, research UX of similar extensions, etc. It needs to do web searches, inspect open source codebases, read articles and papers, etc. I don't prompt to do this exactly, but I expect good models to figure out they need to do it.

3. Then, implementation.

Also, one finding: Q4 and Q8 seem to have very different behavior in this benchmark. Q4 produces 2-3x thinking in the end, and makes more turns - it seems it makes more mistakes, and needs effort to recover from them, while Q8 gets more things right in a first try. In the end, quality is roughly similar, but Q8 gets there much faster, especially the implementation (tried it several times). Could be a difference between concrete artifacts, or between runtimes, I don't know, but be careful - it seems the real-world experience with qwen 3.8 27B can be vastly different, depending on how it's set up.

Regarding DeepSeek 0731 vs Qwen 3.8 27B. On this benchmark, Qwen understand my intent better, it's better at research, and I also liked its implementation more. But: if you're more precise in what you ask, 0731 is also very good, and it's quite a lot faster on mac; raw speed is better, and it needs less thinking to get there. So, I'd say it's a tie in practice, both are awesome :)

> So, I'd say it's a tie in practice, both are awesome :)

Which harness for the benchmark ?

You have previously commented on using OC/GLM. R u going to stock with it?

> I have an internal automated benchmark ... I've been testing various models on it, local and cloud

Once you send your benchmark to "cloud", I don't think you can rely on it being secret/private any longer.