Comment by bakies
2 months ago
I've just started using qwen3.6:35b a couple days ago running on my framework desktop and rather impressed. It runs really well and reminds me of probably the first Claude model I used. It's the first local model that's actually working for me in a coding agent I've tried. Very exciting!
Try 27b, it's significantly smarter than 35b-a3b (although it is slower, it's not so bad with MTP).
At least according to gertlabs, Qwen3.6 27B outperforms every SoTA (closed) model at Kotlin: https://archive.vn/RYBCL / https://gertlabs.com/rankings?mode=agentic_coding&language=k...
Interesting. I wonder if there is opportunity to train a set of small model variants to excel at a certain stacks. Eg Qwen3.6-27B for Node + React or Qwen3.6-27B for Rust + TUI
1 reply →
Qwen 3.6 27B is an anomalously strong all-around model for its size, but when we run our evaluations, we generate 10 coding submissions/language/model (110 total). So full discosure, the per-language per-model performances can be noisy (I do not think Qwen3.6 27B is better than Fable 5 in agentic workflows when writing Kotlin, given enough samples, although we do find some interesting anomalies that hold up under large sample sizes).
It is, but it's way too slow on a Strix Halo due to its limited bandwidth.
(I'm still sad that they didn't make a 122B-A10B version of it, as it's the kind of model that fits best on a Strix Halo, and for 3.5 it was comparable in performance to the dense 27B version).
Yeah the speed is vastly different but it's getting ~10 tps. And the MoE model is like 50 or something. Might use it if it proves to be much smarter I dont really monitor my agents while they're running.
3.5's 122-A10B is still great!
Hmm, I just assumed bigger was better. How's it different?
Off the top of my head since it seems to be the quick info you're looking for: IIRC, with these two, the 27B is a dense model, meaning it's all active at inference. Meanwhile, the 35B is a Mixture of Experts (MoE), so only part of its network (3B?) is active at any time.
1 reply →
35b-a3b is only 3b active parameters, it's a MoE.
I'm running qwen36.:35b:iq4 IQ4_XS quant. Takes 18 GB of RAM with 131k context window. Seems to be really good. Have it running local stuff via Hermes, using a cloud model via Ollama (Deepseek V4-Pro) for heavy lifting.
If your framework desktop is the 128G Strix Halo, I recommend giving Qwen 3.5 122B-A10B a shot.
This Q5_K_M quant should be near lossless and fit with full 256K context in about 100GB of RAM: https://huggingface.co/AesSedai/Qwen3.5-122B-A10B-GGUF
3.6 scores better on coding across the board.
Edit: specifically Qwen 3.6 27B beats that on coding and agentic workflows.
Vibe thinker also beats Opus 4.5
I'll keep this in mind.
Could you please share which coding agent you are using with it?
Crush: https://github.com/charmbracelet/crush/
The Q8_K_XL MTP model from Unsloth: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF
I settled on opencode after trying goose and aider as well. I'll probably try some more but opencode worked similar to Claude code which is my main agent.
I serve the model with ollama and am thinking about replacing ollama but haven't looked into it.
I have openwebui for chat if I want that too, but don't really use it.
I am using Mistral Vibe.
npx @oh-my-pi/pi-coding-agent
Pi