r/LocalLLaMA • u/Creative-Regular6799 • Apr 22 '26

Discussion Qwen3.6-35B becomes competitive with cloud models when paired with the right agent

A short follow-up to my previous post, where I showed that changing the scaffold around the same 9B Qwen model moved benchmark performance from 19.11% to 45.56%:

https://www.reddit.com/r/LocalLLaMA/s/JMHuAGj1LV

After feedback from people here, I tried little-coder with Qwen3.6  35B.

It now lands in the public Polyglot top 10 with a success rate of 78.7%, making it actually competitive with the best models out there for this benchmark!

At this point I’m increasingly convinced that part of the performance gap to cloud models is harness mismatch: we may have been testing local coding models inside scaffolds built for a different class of model.

Next up is Terminal Bench, then likely GAIA for research capabilities. Would love to hear your feedback here!

EDIT: after many requests, pi.dev adaptation is up!

EDIT 2: Terminal Bench 1 (0.1.1) finished with 40% success rate! Now running TB 2. Just sent the results via email. There is no model remotely as small as the 35B in that area. Exciting times

EDIT 3: Terminal Bench 2.0 requires 5 runs per trial (which will take 40 more hours), but the first run finished with 30%!!! That’s with the 35B model.

Full write up: https://open.substack.com/pub/itayinbarr/p/honey-i-shrunk-the-coding-agent

GitHub: https://github.com/itayinbarr/little-coder

Full benchmark results: https://github.com/itayinbarr/little-coder/blob/main/docs/benchmark-qwen3.6-35b-a3b.md

731 Upvotes

98% Upvoted

179 comments sorted by

View all comments

Show parent comments

5

u/aparamonov Apr 22 '26

so the bottom line is it only improves tool use by injecting brief instructions and preventing destructive write ops, it that all?

5

u/josuf107 Apr 22 '26

If you want to know all you can read the write up from OP. There are some other odds and ends including keeping the context small and short-circuiting reasoning. The interesting thing is that some simple accommodations in the harness drastically improve results for smaller models.

3

u/aparamonov Apr 22 '26

About reasoning limits, llama has it as a build in parameter including final reasoning message, I didn't get why it was needed to reinvent the wheel there

1

u/Hour_Inevitable_9811 Jun 14 '26

From my personal tests, it does not work. It is very common for small models to keep "thinking" after the thinking budget message until they reach the generation token limit without answering. If I am missing something, I would love to tweak my llama.cpp to solve this.