returnity

Searching for 3.8 35B: Qwen3.6-35B-A3B (Testing 4 Finetunes vs. Base)


**TL;DR -- You should probably just use base Qwen3.6-35B, as only Occamy-1.0 is competitive with it. Tiel is a major let-down.**
 This post is long. Sorry, lots to cover.


I think we all want to see a next-generation small MoE from the Qwen team to replace 3.6-35B in our workflows. This model is a perfect fit for smaller gmaing laptops and mid-tier rigs. It sucks that Qwen seems to have abandoned this model, but at least there are fine-tunes that improve upon it... right?


Well... maybe not. I ran benchmarks on the 3.6-35B-A3B base model, as well as five finetuunes: Occamy-1.

TL;DR -- You should probably just use base Qwen3.6-35B, as only Occamy-1.0 is competitive with it. Tiel is a major let-down, worse than Ornith. KAT surprises (good), Nex surprises (bad). This post is long. Sorry, lots to cover.

I think we all want to see a next-generation small MoE from the Qwen team to replace 3.6-35B in our workflows. This model is a perfect fit for smaller gmaing laptops and mid-tier rigs. It sucks that Qwen seems to have abandoned this model, but at least there are fine-tunes that improve upon it... right?

Well... maybe not. I ran benchmarks on the 3.6-35B-A3B base model, as well as five finetuunes: Occamy-1.0, Ornith-1.5, KAT-Coder-V2.5-Dev, Tiel-Coder, and Nex-N2

TL;DR - Swift Flash is a killer model that massively reduces excess reasoning. Try it out!

If you haven't seen from my previous comparison posts, I'm a huge fan of the Swift Qwen3.8 models. I've been using 27B since it dropped, and I'm really impressed with the performance and quality (v1.5 is even better). The reduction in overthinking is a huge win, and quality seems to be essentially equivalent in real-world use and benchmarking. The time savings are massive.

When UkisAI told me they were planning to release a Swift Fla

With the release of ThinkingCap-Qwen3.8-27B, I thought it would be worthwhile to do a comparison between the original Qwen3.8-27B, the new ThinkingCap, and Swift-Qwen3.8-27B. Both Swift which I already reviewed, and ThinkingCap do exactly the same thing: they reduce the excessive reasoning loops that 3.8-27B is renowned for. In fact, their claims are almost identical: both models claim to reduce reasoning tokens by approximately 40%, with minimal degradation in performance. I wanted to