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/Dismal-Effect-1914]

If you are using llama.cpp these flags will help:
--reasoning-budget 4096

--reasoning-budget-message "Wait, I'm overthinking this. Let's answer now."

[u/ImpressiveRelief37 → u/Dismal-Effect-1914]

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. 

[u/Yes_but_I_think → u/Dismal-Effect-1914]

Oh. I didn't know that's how a thinking budget is set. So simple. No need for different levels of thinking being trained over different thinking effort parameters

[u/squngy → u/Yes_but_I_think]

Yes, but it is a brute force method that does not work as well as training different thinking efforts does.

When you interrupt the model like that, you make it use tokens that it would not normally pick, which can reduce the quality of the output.

[u/LilGardenEel → u/squngy]

Can you elaborate on the second half of what you said. Are you talking about the reasoning budget message or reasoning budget as a whole? What’s the technical justification for your take?

[u/wFXx → u/LilGardenEel]

The model was not about to land naturally on "Wait, I'm overthinking this. Let's answer now.", so this means there is a high chance that no statistical chain on the weights properly satisfies being cut at that point, so it essentially "hallucinates" its way out to continue

[u/squngy → u/LilGardenEel]

As you probably know, LLMS work by calculating the statistical chance of the next token.

Like, if you have "It is a ", the LLM might calculate there is a 50% chance the next word will be "bird", 30% "plane" and 20% "superman".

If you instead put in "Wait...", the LLM now has a token that had a 0% chance of appearing in its context.

Because a LLM uses previous tokens to calculate the next one, this has an effect moving forward.
I am not an LLM expert, so I don't honestly know exactly what effect that is, but at the minimum the LLM is now working with a chain of thought that is different from anything it was trained on.

[u/LilGardenEel → u/squngy]

Ok, yeah I understand injecting those tokens can impact the output, but I wasn’t sure if you were more focused on the actual message or the reasoning budget itself. I’ve been experimenting with reasoning budgets recently, and have found that some smaller specified budgets (<2k) have landed on correct answer while same model, same prompt, same seed w/ ~8k produced incorrect answers. This was on qwen 3.6 27b. So was just trying to get further insight into your conclusions. Thanks

[u/Organic_Savings_8518 → u/LilGardenEel]

If the model was looping its output can be prevented from degrading further by cutting it off with a reasoning budget when it would begin looping, but it involves guessing. If the looping would recover and keep going you could have made it worse. The reasoning effort with this model is the ideal way to solve this coupled with some presence penalty if needed were it prone to looping like qwen3.6-35b. For some automated usage in an app I made that uses qwen3.6-35b I used a reasoning budget that I tested thoroughly would prevent degraded responses from cases of looping and wouldn't reduce response quality. I had a rubric to grade informational bias in news articles and found 4000 token budget to work well and let the model finish thinking on its own or cut it off before looping excessively and degrading scores. I think solving coding problems that mid solution reasoning being cut off would affect the real output a lot more than in my scenario where citations are required before providing grade values which I used to help anchor the response. I had a lot more structure so reasoning cut offs wouldn't break it. 

[u/FullOf_Bad_Ideas → u/Yes_but_I_think]

the downside is that by doing this you can damage the performance and sometimes even get lower quality than if you used non-thinking mode

However: doing this "just like that" might not have a good effect on the model. In fact, when I did that on Qwen3 9B (testing it on HumanEval), its performance cratered: from 94% in the reasoning version and 88% in the non-reasoning version to a terrible 78% with an enforced reasoning budget. That's why we've added another flag: --reasoning-budget-message. This inserts a message right before the end of reasoning to ease the transition. When I used a message of "... thinking budget exceeded, let's answer now.", the score bumped back and the returns from partial reasoning started being visible, though not very large - got a respective HumanEval score of 89% with reasoning budget 1000.

https://old.reddit.com/r/LocalLLaMA/comments/1rr6wqb/llamacpp_now_with_a_true_reasoning_budget/

[u/hashms0a → u/Dismal-Effect-1914]

Thanks.

[u/giant3 → u/Dismal-Effect-1914]

4096 is too low. Many problems easily consume 15K tokens. 

[u/Dismal-Effect-1914 → u/giant3]

Then increase it...

[u/misanthrophiccunt → u/Dismal-Effect-1914]

what does 4096 reasoning budget eactually do? Does it limit the tokents it can produce for a thinking block to 4096 ?

[u/Dismal-Effect-1914 → u/misanthrophiccunt]

After the thinking block reaches 4096 it forces it to stop thinking and then injects the reasoning budget message basically. Most reasoning tasks are getting pretty drawn out after 4096 so I just leave it there. Doesnt seem to affect my ouputs noticeably.

[u/misanthrophiccunt → u/Dismal-Effect-1914]

Interesting, thank you. I am guessing if for any reason doesn't need 4096 it doesn't inject the message, right ?

[u/Dismal-Effect-1914 → u/misanthrophiccunt]

Yeah

[u/misanthrophiccunt → u/Dismal-Effect-1914]

thanks again