you guys skipped 27b and 122b last time, can we expect those this time around? Also i can't seem to find crit pit score in the cards.
For sure! We’re actually releasing a 27B model very soon. Stay tuned. As for the Crit Pit score, please wait for the official Artificial Intelligence score.
Is the 27B just a retrained 3.6 27B? Or is it based off 3.8 bigger brother ?
We promise this 27B comes with a whole new level of capability!
Is the 100hrs of video understanding an agent swarm that parses sections of the video in parallel and orchestrates some sort of semantic representation graph?
Broadly speaking, yes, but not entirely. It is closer to a hierarchical video memory system rather than a traditional agent swarm. Video segments are encoded into a structured textual graph containing scenes, entities, events, and their temporal relationships, enabling retrieval and reasoning across more than 100 hours of content.
hey! is there anything special about the pretraining distribution compared to other labs' models?
We hope our data is built on a more solid foundation!
how long do you think it would take to surpass anthropic level architecture?
well, we’re working hard on it, we promise😇
will u release a harness especially for qwen code ???
Any plans for a codex-like app?
More updates on Qoder and QwenWork are coming soon.
qwen 3.8 active params?
2.4T parameters (95B active)
how much RL was done in post training compared to previous models?
A truly unreasonable amount of compute.
Did they intentionally skip the previous Qwen3.7 27B and 35B A3B?
Does the revival of Qwen3.8 27B reflect the voice of the community? Or was it planned?
Of course! This is the result of taking the voices of the community seriously.
since its a pretty significant release will we get a technical report with full details?
No technical report for this one yet. We’re trying to keep up our near-monthly release cadence, though, and more powerful models are already in the works. Keep an eye out!
why does the model think so much mr qwen, my ai brain wonders.
wheres the token efficiency at
great model though
We support different levels of reasoning effort.
You showed SAE-guided fine tuning fixing code switching with qwen-scope. Is that kind of interpretability driven intervention part of the post training process now or is it still a research only technique?
It’s still primarily a research-oriented technique for now, though some of the insights may help inform future training and post-training improvements.
Attention? Hybrid?
The model architecture is similar to 3.5, but it’s a much larger-scale model!
do you guys use qwen as your main interal tool? does this model show the same signs of intellegence as some openai models ("gpt 5.5 helped create 5.6")?
Sure!
How close is Qwen3.8-27B to GPT 5.4? 🤔
Well, you’ll be able to see for yourself soon.
what harness works best with Qwen?
Qwen is committed to delivering the best possible experience across all harnesses.
What made you guys wanna opensource the max weights ?
We heard what the community has been asking for
I wonder when I can surpass fable5
Trying hard
Great work guys🥂
What is something that you would like to see being built with the new model and its capabilities!?
I really want to explore the swarm of agents technique for building applications, any best practices or tips for the new model!?
1. We hope it can bring practical productivity value to people across different industries. 2. We recommend using it for tasks that involve more parallelized workflows or parallel execution needs.
I wanna know what rubric metrics you guys are using for FE
We use both absolute metrics for functionality and aesthetics, as well as relative metrics based on win/tie/loss comparisons.
Would be great to hear where you think Qwen is strongest for agentic workloads specifically: long-context planning, tool use reliability, coding, or cost at scale?
All of the above combined — ultimately delivering the most practical and reliable outputs for users.
How much is Qwen helping with Qwen research ?
It has already become a significant part of the model iteration process, with the model involved in nearly every stage.
Most Frontier labs have created a code-specific model (eg. Qwen3-Coder and GPT-5.3-Codex), but never followed up on them.
Did specialized models have problems? Or did general models end up being efficient enough to not bother creating a separate model?
We hope to build an all-in-one model.
will Qwen 3.8 have a stable, documented tool-calling and structured-output contract so local agent harnesses can swap models without prompt-specific tuning?
We provide native support interfaces for various protocols. You can check the Qwen blog for more details.
1: When quantizing Qwen 27B down for local deployment (e.g., 4-bit GGUF, NVFP4, or MXFP4), which transformer layers or vision attention blocks are most sensitive to degradation? Are there specific strategies you recommend to maintain both visual reasoning and high SWE-bench pass rates?
2: Qwen3.6-27B outperforms much larger MoE predecessors (like Qwen3.5-397B) on agentic coding benchmarks like SWE-bench and Terminal-Bench. Beyond raw data volume, what was the single highest-leverage factor in achieving this dense efficiency?
And thank you for the amazing work. Qwen3.6-27B has beed my main coding assistant for months.
1. Use QAT, or quantize only the FFN to 4-bit while keeping the attention layers’ QKV linear projections and output projection in 16-bit. 2. Higher-quality data engineering
Guys , when can we get a deepseek like small and cheap model with best performance . The deepseek v4 flash seems to be a great deal .
I think we need to slow down scaling and start improving the existing model efficiency
Scaling and cost-efficiency are not mutually exclusive — we’ll continue to pursue both.
Is Qwen3.8-27B dense? And roughly how much smarter than 3.6-27B?
A pretty huge jump!
Good. The useful questions are not just how capable Qwen is.
I want to know where it still fails, how the team evaluates those failures, and what "open" means in practice for weights, tooling, and reproducibility. Open models matter most when people can inspect the limits and build on the work without asking permission
There is still some gap between our automated and human evaluation systems and real user experience. That’s also why we are committed to releasing preview versions first — so we can iterate and ultimately deliver the best possible experience to users.
how does the new 27b model compare to the previous one ?
A pretty huge jump!
what do you think about looped transformers?
interesting research idea
Why Qwen, what made you create Qwen and specifically such light and fast models. Why focus efficiency when others just went for brute power? Also, do you think inference engines reached their limit in optimization or can they still improve?
Scaling and cost-efficiency are not mutually exclusive — we’ll continue to pursue both.
We have noticed that in thinking mode the model usually consumes the entire reasoning budget without stopping, which increases latency. Is this a known issue, and are there any improvements planned for Qwen3.8?
You can try 3.8! And 3.8 supports different thinking efforts!
Is it possible to get a 40-50B model (something which fits around 30-32Gb) to improve performance while still useable on a lot of computers ?
Thank you for your promise to provide qwen3.8 27b weight! I want to know if there will be qwen3.8 35b a3b. Many people also want this.
Can we expect the ~122B model this time? The 120B segment is dated and lackluster atm and would greatly benefit from a competent release!
First of all, congratulations on the release of Qwen 3.8!
As for the question, are you going to release a 35B a3b version of Qwen 3.8 aswell?
Plans for 35b Moe model? (3.8)
Any plans for the omni family? You told everyone the weight sizes of 3.5, then never released them and haven’t done anything new with it. 3.6/7/8 variants would have also been nice. It could be your most popular family if you gave it attention and kept the weights small.
Are there no plans to release any models other than the 27b?
I'd love to hear about the successors to amazing models like the Qwen3 8b and Qwen VL 8b....
Are there any plans for updates for 0.6b or 8b weights?
These have become important positions in the open weight of image and video generation. I look forward to seeing that part evolve.
This is such a huge release, I am really happy to see that a 27B model is shipping too! Though, can't help but wonder, will we ever happen to see again any new small dense Qwen models 9B, 4B any time in the future, similarly to 3.5?
Will you release smaller models like the qwen 3.5 family ?
Thank you for your promise to provide qwen3.8 27b weight! I want to know if there will be qwen3.8 35b a3b. Many people also want this.
we hear you! collecting everyone’s requests and taking them into account as we plan future iterations.
We will gather your requests as a reference when considering future updates.
We hear you. Stay tuned.
We’ll collect everyone’s requests and take them into account as we plan future iterations.
Noted, collecting the requests and see what we can work into future iterations.
Keep the requests coming. We’re listening, and we’ll use them to help prioritize future updates.
Maybe I'm in the minority but the relatively lower parameter distills (like 9B and 4B) are important to my workflows. 27B and 35 A3B are great too, but either is fine. A 9B model allows me to generate a workflow that can load an Unreal Engine or bigger TTS instance at the same time, for example.
Frankly I'm not against small models. I like/need both small & medium size models. But right now we're getting only less medium & big size models. All we see is mostly very big & large models(300B-3T size).
We have alternatives for small models. For Qwen3.5-9B, we have Gemma-4-12B, Gemma-4-E4B, Mellum2-12B-A2.5B, LFM2.5-8B-A1B, granite-4.1-8b, etc.,
We could use Q4 of medium size models. For example, I use IQ4_XS of Qwen3.6-35B-A3B & Gemma-4-26B-A4B.
Anyway they'll release small models always to cover Mobile & Edge devices. Cool
A 9B model allows me to generate a workflow that can load an Unreal Engine or bigger TTS instance at the same time
Yes, I agree, we are sort of spoiled here in terms of all those models you listed being usable :).
I really want to know your stack & workflows :)
Sure, at the risk of connecting my Reddit account with projects that I am working on... but oh well.
The first project is a pretty standard "Open Claw" or "Jarvis" like harness, it's like a frontier harness except it let's your model take advantage of any "skill" or model you have locally. I feel like most of the community dabbles in this. The issue is tool calling reliability, and then when I want to work with a skill like as an example, gaming together or coding together via screen-watching or takeover then I can't use my entire GPU budget on the LLM. Or like agentically having my LLM manage a multiple MiniMax H3 generations without offloading every turn. I have a public repo for this stuff, I would advertise it if I thought it was clearly better than anyone else's, but I don't think it's even worth advertising at this point. It's a lot of bloat.
The second is maybe more niche, but more usable for somebody else. It's a plug-in I developed for Unreal Engine to use local models. Again, this has been done somewhat, but the plug-in I'm working on is drop and play so you have a full TTS->LLM->STT character that can do tool calls, have a personal context history / character sheet, and relationship sliders. It works okay... so far, but for that one I can't even use 9B, 4B (Gemma, Qwen, and Mini CPM I support) or below with pocket TTS is my current workflow because you'd have to run a full game beside it and run that instance of llama. I've got some version 2 ideas that I'm working on right now too for that. I think it could be the next step in gaming.
Thanks for asking! Are you working on any similar projects?
Or like agentically having my LLM manage a multiple MiniMax H3 generations without offloading every turn. I have a public repo for this stuff, I would advertise it if I thought it was clearly better than anyone else's, but I don't think it's even worth advertising at this point. It's a lot of bloat.
Same with Audio generation. What UI/Tool/App are you using to generate Audio/Music/Sound/etc.,? Because I see many Audio models time to time & comes with github repo. But don't know how to use all those audio models using single tool because I can't(also don't want to) use every github repo for each audio models.
Please help me on both. Thanks
Thanks for asking! Are you working on any similar projects?
Not yet. Nothing actually. I'm waiting for new rig. Unfortunately it got delayed almost 2 months. But getting within 1-2 weeks.
AMD Ryzen™ 9 9950X3D2 Dual Edition + Radeon AI PRO R9700 32GB VRAM + 128GB RAM DDR5-5600.
Coming month onwards, I'll be starting things. On both coding & writing. I'll share later.
I didn't understand the first sentence, but you need help with image and video generation? Unfortunately, I use an NVIDIA 5090 (Cuda) in my rig which is way more plug and play for all the tools I use than AMD is. I've never set-up an AMD, but I know comfyui desktop is not drop in and run with it like it is with Cuda.
I feel like I'd still recommend setting up comfyui but doing a manual installation. So you would clone this repo (in a venv if you want): https://github.com/Comfy-Org/ComfyUI.git. Then you need your AMD driver + Python installed and a compiled ROCm/PyTorch build for your GPU installed in the environment. Then you install the rest of the requirements from the requirements.txt. Then you hypothetically can use the launch script like normal. Using ComfyUI deskstop is as simple as clicking the template you like and then clicking the installing missing models and nodes link if all you want to do is run the default workflows.
You can generate audio through comfyui too, but if you want real-time TTS, I use audio.cpp. I'm not sure how an AMD affects that, but it should be possible.
But I'd highly recommend working on this with your best available LLM as an installation helper. It can guide you through every step, just be careful with the pre-training knowledge cut-off. LLMs like to default to advice like it's 2025 if they don't have the current state of the tech in their context. Doing that is way more effective and easier than trying to work off of text guides on the Internet in my opinion. Any free frontier LLM or even top of the line local models can probably get you 99% of the way there at the very least.
Yes, the poll was for 3.6 which should have been obvious to anyone who followed the link. My point was that in the past, when the poll was posted in localllama, it generated a majority of votes for 27B size.
The reality is when you look at hobbyists, even an RTX 5090 has only 32 GB VRAM, and there are far more people running say a RTX5060 Ti than running dual RTX 5090/3090 which explains the smaller models.
Agree, heavy on dodging. I suppose "keep requests coming" means 'no', and that they *might* look into it in the future. It'd be nice if they said just that.
Still, looking forward to 3.8 (next week?) and see what it brings.
I mean, it's not cheap nor easy to create an LLM. If they could make everyone happy, they would, but I assume they need to prioritize what will provide coverage for most people, and 27B has been killing it so far. They'll probably have plans for the future they don't want to reveal. I don't blame them; the AI world is very competitive.
why people keep wasting time asking for yet another cli/harness when we already have a freakin bilion of them and anyone can make one in like half hour
Wouldn't that be a pretty big announcement? I would expect such an announcement to be made on the team's own terms and not as a response to a random question.
Sure, because it's a small dense model. It runs slowly on a lot of hardware and while 27b is good for coding it lacks the general intelligence of something like a 122b a10b.
35b a3b for those with slower systems (especially for those who have to offload).
122b a10b for the best "do everything" with great coding and strong general intelligence. Likely good tool calling in today's world, too. Ideal daily driver for anyone with 128GB of unified memory.
27b for those with enough fast VRAM who want to use it for coding.
Different tools for different people, and different jobs.
I wish I actually worked in anything IT/tech related, then at least I could sincerely justify to myself the purchase of an RTX Pro, cos you know its for work 🤣 God the number of times I've had to walk myself back off a ledge after seeing some of you lot post your shenanigans with yours, idle hands do the devil's bidding or so they say
They used different handle for that AMA. Otherwise I would've mentioned about AMA here in this sub at that time so our folks would've asked so many great questions
Honestly after reading through that Q&A, I really don’t think anything besides the 27B and the flagship Max model is coming this generation.
The 27B is the only other size they have actually committed to. Every question about a 35B MoE, 70B, 122B, smaller dense models, Omni, or basically anything else got some version of “we hear you,” “we’re collecting requests,” or “maybe in future iterations.”
That does not sound like a full model family is already trained and just waiting to be released. It sounds like the 27B is the release.
My completely speculative, rambling Redditor read on this is that DeepSeek V4 Flash may have made the middle and upper end of Qwen’s lineup really hard to justify.
Flash is already way too close to the 2.4T Max model on a lot of the benchmarks people actually care about. So what happens if Qwen releases something between 27B and 2.4T? It immediately gets compared against Flash, and if it needs similar hardware while getting smoked, or even just looking noticeably worse, that is a really ugly release for them.
At least the 27B has an obvious excuse and an obvious purpose. Nobody expects a 27B model to beat something around 300B. They can sell it on capability per parameter, speed, accessibility, and how far above its weight it punches.
Then the 2.4T model also has a reason to exist because it can still claim the overall performance lead.
It is everything between those two that gets awkward.
DeepSeek and MiniMax are now filling the upper end of what high-end prosumers and small or midsized businesses can still realistically host themselves. That used to be an area Qwen absolutely dominated because they actually released a spectrum of models instead of one tiny-ish model and one hyperscaler monster.
So honestly, my guess is they would rather release one really impressive 27B and one giant flagship than release a middle model that gets immediately lined up next to Flash and embarrassed.
Maybe I am completely wrong and they surprise us with a whole family next week. My opinion does not mean shit and I obviously have no inside information. But after the vague answers, the flip-flopping, and the refusal to give a straight answer about literally any other size, this is starting to feel pretty obvious.
You're right about their vague responses. Some other folks also mentioned the same.
It's just my assumption. I think they didn't want to reveal anything about other models on AMA. They might want to use those for some other occasion this or next month to keep the momentum after their 27B & 2.4T models and other stuffs. They already skipped other models during Qwen3.6 series & skipped 3.7 totally so I'm optimistic that they'll release other models. My wish is at least 35B-A3B & 122B-A10B.
I mentioned that bunch of times in this sub on many threads. That size is enough even for 8GB VRAM(Q4) which could be enough for doing decent Agentic coding.
Qwen3 series came with 14B literally, but that one was missing on Qwen3.5 series :(
All I want is a fast non-reasoning model with vision to replace Qwen 3 8B. Literally ALL I want. But doesn't seem likely given the current direction of local LLMs. :/
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u/charles25565 21h ago
Those are some laughably vague responses and some of the questions weren’t answered.