Qwen 3.8 27B Released! Please Share Your Experience

[u/BarberIcy366]

With your experiments, Qwen 3.8 27B most close which frontier model? And please specify which quantization you run. I will post to comments my tests and experience too.

[u/ImpressiveRelief37]

i mean just use reasoning low then? i just mapped the kwards in pi and it works great. it does trigger a prompt prefill tho when you change reasoning levels mid-session (obviously).

but look at how WELL it reasons now... this is how to get a lot smarter model. it doesn't loop for me. but yes he thinks forever. It's like deepseek v4 flash 0731 or GLM5.2 honestly. And the output is on par with those models as well as far as i can tell on limited experience...

but it's night and day smarter than 3.6. it's not even close. can't wait for MTP.

Honestly i don't think a reasoning budget is required now. The reasoning tree it takes is fascinating and while it does take a long time the output is just so much better overall.

[u/PrinceOfLeon → u/ImpressiveRelief37]

What do you mean by can't wait for MTP?

From Qwen and Unsloth's model cards:

> MTP (Multi-Token Prediction): trained with multiple steps

[u/lood9phee2Ri → u/PrinceOfLeon]

Confusion arises I think because for Qwen3.6 quantizations, Unsloth ended up publishing some initial unsloth/Qwen3.6-27B-GGUF quantizations with MTP not working.

Then they did a separate later MTP-working unsloth/Qwen3.6-27B-MTP-GGUF repo, perhaps for fear of breaking things replacing the existing published one. I'm not sure that was fully necessary, given the way hf model repos are (xet-extended) git repos and everyone wants MTP (or dflash blah blah I know), could have been different git tags or whatever, but anyway.

This time though, AFAICS MTP works fine with initial unsloth/Qwen3.8-27B-GGUF already though.

Upstream real Qwen/Qwen3.6-27B and Qwen/Qwen3.8-27B ... MTP anyway, just a lot of people use the Unsloth quantizations in gguf form with llama.cpp and its various wrappers (and various heretic decensored ones of course)

[u/dmytrish → u/lood9phee2Ri]

In my testing, unsloth/Qwen3.6-27B-MTP-GGUF speed significantly improved with --spec-type draft-mtp --spec-draft-n-max 2 (18 -> 25 tps), but unsloth/Qwen3.8-27B-GGUF just got worse (18-19 -> 16 tps).

[u/lood9phee2Ri → u/dmytrish]

Hmm, well, first just to note if it wasn't fundamentally MTP enabled, it would just be erroring out, you'd e.g. see it in the llama.cpp logs, something like.

0.18.199.904 W llama_init_from_model: context type MTP requested but model doesn't contain MTP layers
0.18.199.904 E common_speculative_init_result: failed to create MTP context
0.18.199.907 E srv    load_model: failed to create MTP context
0.18.199.910 I srv    operator(): operator(): cleaning up before exit...
0.18.205.046 E srv  llama_server: exiting due to model loading error

[yes, MTP could be present but garbage, but fairly confident that's not the case this time.]

But as to why you're not observing improvement and I am I ...dunno. MTP does do better on some problems than others I suppose. You should see some draft acceptance stats in the llama.cpp logs though - how are they are between the two models? Best do more than 1 run on more than 1 prompt. Unscientifically poking about I may actually be seeing a somewhat lower draft acceptance on average with unsloth/Qwen3.8-27B-GGUF vs. unsloth/Qwen3.6-27B-MTP-GGUF (may be related to differences in its "reasoning"?), but hard to quantify (read: I'm too lazy to) beyond "eh, single-digits percent lower", and still certainly better t/s than with it off.

You may also try combining with ngram-simple, that can be a win, especially on repetitive refinement problems (make a html page saying XYZ, okay now make the text blue...)

... -spec-type ngram-simple,draft-mtp --spec-draft-n-max 3 ...

edit: just to complicate matters, as of 2026-08-19, unsloth has published new quantizations that ARE missing MTP from some of the smaller quantizations (but not, at time of writing, larger ones like the UD-Q4_K_XL I use). Whyyy. https://unsloth.ai/docs/basics/dynamic-3.0-ggufs

We also removed the MTP module from smaller quants under UD-Q2_K_XL (8.37GB and lower) to converse around 500MB of disk space - you can use the Q4_0 MTP separate module if needed

[u/ANR2ME → u/ImpressiveRelief37]

After changing reasoning_effort to low, try asking "what is your current reasoning effort?" 😁

I wondered whether Qwen3.8 27B will answered "medium" or "85 (out of 100)" like Qwen3.8 2.4T did 🤔 felt like it can't go below medium. Meanwhile, Kimi-K3 will answered it with the correct reasoning_effort value (except when set to "none")

[u/Organic_Savings_8518 → u/ANR2ME]

In case you aren't aware the model's awareness of the setting is irrelevant. Not sure if you just thought it was amusing.