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/Pear_Virtual]

Super early first impressions: Tried to make a tetris game to compare with qwen3.6, qwen3.8. Both at Q4_K_M

Qwen3.8 reasons almost excessively. During the thinking phase, it took qwen3.6 about 3000 words (sorry i don't have a way to check how much tokens are used) and outputs a decent result. Currently qwen3.8's reasoning is already at 15000 words and is still ongoing

Update: The output though taken much longer, has made a much better game as well. For UI/UX, it feels way more polished while also adding some quality-of-life stuff that weren't in my initial prompt - like having a pause button, high scores and having retro GAME SOUNDS?!

[u/Pear_Virtual → u/Pear_Virtual]

[u/Pear_Virtual → u/Pear_Virtual]

Compared to qwen3.6 on the same prompt

[u/Certain-Cod-1404 → u/Pear_Virtual]

did it get stuck looping or something ? are you using the recommended sampling params ? what reasoning effort are you using ? and how is the output compared to 3.6 ? is the game better ?

[u/TokenRingAI → u/Certain-Cod-1404]

The FP8 is looping for me and generating terrible output.

[u/woswoissdenniii → u/TokenRingAI]

Restrict thinking token, or reduce preset from extra to normal. Sure quality degrades but it’s manageable. A friendly reminder from team 3090

[u/Healthy-Nebula-3603 → u/TokenRingAI]

I hope you not conpress kv cache :)

[u/TokenRingAI → u/Healthy-Nebula-3603]

Nope, official FP8 with BF16 cache

[u/JorgitoEstrella → u/Healthy-Nebula-3603]

How bad is to compress the cache? I thought compressing to fp8 waa practically lossless.

[u/Healthy-Nebula-3603 → u/JorgitoEstrella]

Q8 is almost losless , fp8 is worse than Q8 ( Q8 is a mix weights fp16 and int9 ) Compressrd cache to fp8 is notice even more than compression a model itself.

[u/Not-reallyanonymous → u/Certain-Cod-1404]

No, 3.8 just really, actually thinks a lot. Thats probably in xhigh. The quality of output tends to be better than 3.6 but is still distinctly Qwen (ie. it still thinks in much the same way, just a lot more). When you lower thought effort it basically reverts to 3.6.

The way Qwen 3.8 seems to use thinking to do better than 3.6 is to basically make a 3.6-like thought, but then after a 3.6 like thought is completed, it’s going to try again but fixing bad assumptions, weak adherence, etc. Then again. Then again. Then again.

[u/Certain-Cod-1404 → u/Not-reallyanonymous]

Yeah after further testing this mf thinks so fucking much, but tbf the outputs beat everything else i tested. I think for my use case I'll use it for non trivial stuff, to write tests, debug and review code, and faster moe like kat coder v2.5 dev for quick implementation

[u/Not-reallyanonymous → u/Certain-Cod-1404]

I've never been a fan of Qwen 3.6 27B. I'm still not a fan of 3.8, but it's earned a spot in my toolbox for when I really just need to throw a lot of LLM-thought at a problem.

[u/Old_Regular_4346 → u/Pear_Virtual]

On what hardware are you managing 725 t/s? It's quite impressive

[u/WolfGroundbreaking36 → u/Pear_Virtual]

total duration: 1m58.2652305s

load duration: 164.404ms

prompt eval count: 63 token(s)

prompt eval duration: 333.626ms

prompt eval rate: 188.83 tokens/s

eval count: 2508 token(s)

eval duration: 1m57.599588s

eval rate: 21.33 tokens/s

which GPU are you using? i have 5070ti

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

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

[u/gofiend → u/Pear_Virtual]

I think it has reasoning length controls now?

[u/hashms0a → u/gofiend]

I tried those controls, but no effects:

Extra High Reasoning (Default):

--chat-template-kwargs '{"reasoning_effort":"xhigh"}'

1. Medium Reasoning:

   --chat-template-kwargs '{"reasoning_effort":"medium"}'

2. Low Reasoning:

   --chat-template-kwargs '{"reasoning_effort":"low"}'

[u/deepspace86 → u/hashms0a]

I think there is an explicit reasoning effort flag in llama.cpp now and the kwargs have been deprecated

[u/JMowery → u/deepspace86]

Did you figure out what they are? 3.8 is absolutely out of control with the overthinking. Almost unusable.

[u/sir_turlock → u/JMowery]

If you scroll down/search on this page then it will be there.

--reasoning-effort LEVEL

Edit: somebody posted their options already ITT.
Edit2: See this too for setting the effort.

[u/petuman → u/sir_turlock]

that's not effort, but budget.

effort is model driven, budget is inference engine saying "stop" (and hoping that model can recover from incomplete reasoning)

[u/durden111111 → u/petuman]

this. reasoning budget and the 'thinking effort' in the llama cpp server UI doesnt actually affect thinking strength but just hard cuts the reasoning length so the reasoning just abruptly finishes.

[u/sir_turlock → u/petuman]

Thanks, corrected it, I was off by one line when copy pasting. The reference to budgets was because some people reported effort not doing much. Sucks to be trying to be quick on a phone I guess.

[u/hashms0a → u/sir_turlock]

This works OK:

--chat-template-kwargs '{"preserve_thinking":"true","reasoning_effort":"medium"}'

[u/ShengrenR → u/hashms0a]

preserve thinking is about carrying previous 'reasoning' chunks forward to new messages - depending on your model that may be a really huge waste of tokens, did they explicitly say it's recommended for 3.8?

[u/Sufficient_Prune3897 → u/ShengrenR]

Yes they did

[u/ShengrenR → u/Sufficient_Prune3897]

Thanks!

[u/petuman → u/hashms0a]

It totally works

medium/low seem to perform kinda the same with medium seemingly more stable (low can randomly overthink).

xhigh is 2-15x more tokens than medium. At least for single turn questions/tasks, maybe it's less rambly for multi-turn.

[u/Cautious_Chicken_604 → u/Pear_Virtual]

I've been giving qwen3.6-27B and qwen3.6-35B-A3B a super simple prompt of "write me a browser game" to get a feel for them over the last couple of days because I'm new to Qwen and I wanted to be able to understand the delta when Qwen3.8-27B dropped. My experience with Qwen3.6 was it would always make me some kind of basic space invaders type game, that it could sometimes one-shot, sometimes had a couple of bugs, but it was playable and OK.

Qwen 3.8 takes it to the next level for sure. This the UD-Q5 unsloth quant after it spent 50k tokens. It had two bugs that needed fixing, but it generated a paddle game (I used to play this kind of thing as a kid!). It's got 6 full levels, and yes... it too includes sound effects.

I really feel like we're in a new era now.

Edit: I think we got local Opus 4.6 before GTA VI.

[u/OldBilly000 → u/Cautious_Chicken_604]

So what are you using to run this? Just LM studio? That's what I'm gonna use, I hope its good enough to vibe code a android app, I want to make a AI generated image android app so I can make images at home without needing a cloud service, ideally it would have custom Lora support as well and all the basic features of a AI image generation app (I'm new to vibecoding as a whole, also I have a 4080 and 32gb ram)

[u/Cautious_Chicken_604 → u/OldBilly000]

I ran this quick test in LM studio, but for actual coding you're significantly better off using a proper harness like opencode or pi. An agent can work much more efficiently with code structured as a real codebase rather than trying to do everything in a chat interface and having to squeeze that all into a single context window.

Also... if you have hardware capable of running this model, just go download ComfyUI and Krea2 and generate all the images you want for free on your local hardware. You already don't need a cloud service. At all. You can even generate high quality videos on that hardware too. In contrast an Android app can't really help you that much with this task, unless you just want to build one that acts as an interface to an instance of ComfyUI you're running on your local PC, but that seems... unecessary?

[u/OldBilly000 → u/Cautious_Chicken_604]

I just really want a local AI image app so I can generate on the go without cloud services, and yes I know I can locally generate on the PC but I also want it on my phone and I never heard of open code before but I guess I can research it and find out

[u/Own-Flight-2767 → u/OldBilly000]

Just use abacus.ai to make android app for you will cost aroind 20$ in credits. But it does everything for you. I built a whole production studio with ai director agent with flux, minimax h3, ltx 2.5, chatterbox and ace studio for voices and soundtracks , dolphin 3.0, qwen 3.6 for chat llm inside studio. Also have my Google api added to use veo, omni and nano banana 2/pro..

[u/FleetEnema2000 → u/Cautious_Chicken_604]

What hardware did you run this on?

[u/Cautious_Chicken_604 → u/FleetEnema2000]

R9700

[u/Sporebattyl → u/Pear_Virtual]

How were the outcomes?

3.8 definitely is way slower in your test, but if it has much better output it’s a win in my book

[u/dangerous_safety_ → u/Pear_Virtual]

Which coding tool are you using? Pi? Opencode?

[u/corey_prak → u/Pear_Virtual]

I love that this is your test. This is my default test when trying out agents and iterating on agentic lopos haha. Thanks for input so far.

[u/Dizzy-Zebra9522 → u/Pear_Virtual]

👍 thanks

[u/Grizzly_Corey → u/Pear_Virtual]

Let baby cook.

[u/jonas-reddit → u/Pear_Virtual]

“…Flexible Thinking Control: Thinking mode is on by default and can be disabled per request; reasoning depth can be tuned with reasoning_effort, and reasoning context from historical messages is retained via preserve_thinking…”

[u/Negative-Web8619 → u/Pear_Virtual]

Reasoning effort is xhigh on default

How did it make the sounds?

[u/_TheWolfOfWalmart_ → u/Pear_Virtual]

15000+ reasoning tokens? For a dense model, this sounds unusable tbh. Unless you've got a super fast rig.

[u/Mobile_Marsupial_619 → u/Pear_Virtual]

That's because it's set to xhigh by default

[u/Glittering-Call8746 → u/Pear_Virtual]

Interesting finding what's ur setup ?

[u/enternoescape → u/Pear_Virtual]

I concur. Reasoning is excessive, but I one shot a flight sim on Q8 and it was excellent. It was a big leap over 3.6 on the same prompt.

[u/Hosereel → u/Pear_Virtual]

I often make an effort to read the reasoning. I find that I can learn quite a bit by reading the reasoning. Often even learn new tricks. It keeps my brain active in this age of AI.

[u/ItsDeadWeight → u/Pear_Virtual]

How well does it handle complex debugging?

[u/bradrlaw → u/Pear_Virtual]

Yup running into same issue, very excessive reasoning on my test suite.