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AgentWorld is a pretty recent MoE release from the Qwen folks with quite a bit better performance than 3.6, released around the same time as 3.7 Plus/Max, I guess. Not sure why it didn't get more attention, as it is definitely better than 3.6 35B A3B on all dimensions, but especially for agentic use. Still nowhere near the dense models (even 3.6 27B), but clearly an upgrade in the small Qwen MoE line.

https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B

KAT Coder is another Qwen 3.6 MoE fine-tune that also improves on 3.6 by a measurable amount.

But, I do hope for a bigger MoE, in the 70B to 120B range, something in the Coder Next lineage. I've got a Strix Halo that isn't getting used to its best ability because the best models all run fine on my faster desktop dual 32GB GPU setup, and they run too slow for comfort on the Strix Halo.

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I'm hoping too that they'll put out some MoE variants.

Qwen3.5:122b:a10b can run about twice as fast as this 27b dense model.

Edit: Like its predecessors, 3.8 seems really inclined to overthinking, and on a 27b dense model that's kind of painful. I think I'm going to stick with gemma4:26b-a3b as my go-to because it runs about 4x as fast and tends to only need a fraction of the tokens in its 'thinking' stage to get the same or similar answer.

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"inclined to overthinking," holy cats you're not kidding! On a Mac mini M4 Pro 64GB I prompted it with "svg owl" and it thought for 17m12s, outputting 36.3KiB of thinking chatter. It did end up producing a 20.2KiB HTML+JS+SVG file with a very nice owl, including cursor-tracking animation, but it ran for more than a half hour!

The MoE models are stupendously faster.

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Yeah, that's probably the answer given that it apparently defaults to 'xhigh'.
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Probably helps it score a little bit better in benchmarks :) `medium` seems like a nice balance so far; along with some light steering to vary think effort as needed for task and being pragmatic.
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Did you try the claude reasoning traces finetune for qwen3.6? I find that it works muuch better. I assume the same 3.8 finetune will be released at some pointas well.

Edit: link - https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reason...

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Same here! Qwen3.6-35B-A3B is the only local model I've found that runs reasonably on my iGPU. Looks like me and and my noisily-wheezing laptop will be sitting out this upgrade.
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[self-reply because comment edit window ended]: I now have a head-to-head benchmark. On my wheezy laptop (specs in sibling comment), this new dense model, Qwen 3.8 27B, gets ~4 tokens/second on generation. The older mixture-of-experts model, Qwen 3.6-35B-A3B, gets ~20 tokens/s.

MoE is literally 5x times faster (on CPU) than comparable dense Qwen!

Tested:

Qwen3.6-35B-A3B-UD-Q4_K_XL

Qwen3.8-27B-UD-Q4_K_XL

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Mind sharing your laptops specs? Just interested to see what is needed to locally run Qwen3.6-35B-A3B
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Don't mind! It's 64 GiB dual-channel DDR5-6400, i.e. roughly 100 GiB/s of bandwidth. (AMD 7840U (Zen 4))

I'm using a Q4 quantization from unsloth (Qwen3.6-35B-A3B-UD-Q4_K_XL). It gets up to ~20 tokens/second in generation. I don't know precisely how much KV cache I can safely use, but it's in between 140k–256k. (I.e., 140k reliably works, 256k kernel-crashes from OOM. Don't feel like bisecting).

Inference is llama.cpp with the Vulkan GPU backend on Linux. (I.e., -DGGML_VULKAN=1 on the llama.cpp build, and --gpu-layers all on llama-cli or llama-server. (And for my specific setup, two kernel parameters specific to amdgpu: ttm.pages_limit and ttm.page_pool_size. A driver VRAM limiter. Look it up if you're on amdgpu!)).

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Thanks! I don't have any knowledge of running models locally.

I assume it would not be able to handle an unquantized Qwen3.6-35B or is it irrelevant as you almost always would want to run a quantized version of the model on consumer hardware?

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not parent, but 4-bit quantization is generally consider a good trade off for speed/performance, so you might use it even when you aren't on consumer hardware, but definitely when you are on consumer hardware.
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yeah, that's the A3B part; going up to A5B would probably also feel comfortable.

on the 395+ AI MAX w/128GB, the A10B qwen 3.5 can do a lot of long running work if you don't need to baby sit it. deer-flow works well like that.

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Me too. 35B A3B runs really fast on my MacBook Pro (M4 Max) and is suitable for real-time tasks like dictation post-processing. The dense model is not.
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I'm still confused about Qwen 3.6 35B A3B. Everything I read said that the 27B model performs better at coding tasks, so what's the purpose of the 35B model?
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The "a3b" refers to its active parameters -- unlike 27b it is a mixture of experts model, so it runs much faster, about as fast as a 3b model, but needs as much memory as a 35b model! So good for unified memory systems like macs :)
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It's better for VRAM poor people. I get 4-5 t/s with 27B and 20-30 t/s with 35B A3B.
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anecdotes: 35B-A3B does want more memory, bigger model. But if you get it running it will be faster and more enjoyable to use -- text will fly by -- due to only 3B params being active, in my experience at least.
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Runs faster on bad hardware.
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> I'd gladly take A5B or A8B or even A10B as a sort of middle ground.

Whats up with focusing on the active param count? Do yall fiddle with the weights or something?

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Total param count decides how much vram you need to run it. Active param count decides how fast it runs. My 10 year old GPU can load quantized 35B or 27B, but it can’t process 27B parameters per token faster than 2-4tok/s, while it can do A3B at >40tok/s
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Thank you Kenny
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You can run these on CPUs at a somewhat reasonable speed.
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Or (somewhat) low TDP GPUs for that matter, like workstation ones, that might have enough total VRAM but not the best bandwidth/compute.
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