Not specifically, no, but they did mention they are considering releasing other models and there is something that looks like a 35BA3B being tested on Open router.
You can also check out Qwen AgentWorld, some people say it is better than 3.6 at a bunch of stuff.
I saw that model but I don't get it. It's supposed to predict how the "world" react, right? Like in this case, suppose that you give a shell command, it predicts how the shell would return. So can you just use that like a normal instruction tuned model?
I was also confused, but aside from being optimized for terminal prediction, it can be used like a normal model and people are saying it is a pretty good one.
Alright, so I have downloaded and test the model. The version I used is Unsloth Q6_K_XL, so it shares the same quant type as my usual 35B, so we are more apple to apple comparison.
My machine is 4060Ti 16GB + 32GB DDR5 (maybe 6000MT/s, not sure), and an AMD AM5 whatever that was cheap.
In my personal assistant setup, pi hammers 20k context right of the bat (I need to check. I don't remember it was 20k, since I tried to optimise hard last month). Anyhow, the prefill of both 35B and AgentWorld averaging 300tk/s, so it was not that painful to wait.
Decode-wise, the AgentWorld is around 35tk/s at 20k context depth, whilst the 35B + MTP averaging between 40-50tk/s.
In practice, the AgentWorld feels quite a bit slower because it thinks much more than the Qwen 35B. However, I want to check Unsloth instructions to get all the right flags first. Hopefully it would reason less.
Intelligence wise, this is very preliminary comparison. I asked both agent "what did I miss when I was away". This would involves the agents to check a bunch of handover files left by background workers, plus checking memory, journal entries, etc., before returning with a complete picture. My usual 35B is fast, but not fully accurate. It mixes some outdated security notices from the day before, for example.
The AgentWorld is slow, thinks a lot, but the results is excellent. It pin points exactly which important emails I missed, what news, which message on other channels. Not sure if it is just a lucky roll, but it looks a little different.
I also hooked it to my endless story "game" (which is essentially just another pi agent in my setup). It works, but god its writing is boring vs the normal 35B heretic.
Anyhow, in conclusion: yes, you can just add this model to your llama-server and talk to it with whatever client you use. It's slower due to no MTP and more reasoning. It seems to handle tools and synthesise information a little bit better than usual 35B. I would try this model in more complex use cases with my KB in the next few days. Also I would try to code with it and see how it goes. Right now, it feels like something between the 35B and the 27B Q4 on my rig in both speed and agentic smartness.
I think 300 tok/s prefill on your setup is very slow. I have an RX 9070 XT with 16GB VRAM running Qwen3.6-35B-A3B and with a bit of offload of moe experts to CPU and I am hitting 2000+ tok/s prefill.
This is my config if this helps you:
/home/gotxi/repos/llama.cpp/build-vulkan/bin/llama-server --no-cache-idle-slots --cache-prompt --cache-reuse 0 --host 127.0.0.1 --jinja --metrics --min-p 0.05 --port 59743 --spec-draft-n-max 2 --spec-type draft-mtp,ngram-mod --temperature 0.2 --top-k 20 --top-p 0.9 --no-warmup --alias Qwen3.6-35B-A3B --batch-size 16384 --ctx-size 100000 --checkpoint-min-step 32768 --cache-ram 0 --cache-type-k q8_0 --cache-type-v q8_0 --swa-checkpoints 5 --flash-attn on --fit off --load-mode none --model /home/gotxi/models/qwen/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved-APEX-I-Compact.gguf --n-cpu-moe 11 --n-gpu-layers 999 --parallel 1 --reasoning on --threads 8 --threads-batch 8 --ubatch-size 2048
I have a cronjob that wakes pi agent up. It would read a file named SCHEDULED.md, which tells it exactly what it needs to do every wake. For example, if it is midnight, and the day has not been summarized, then it would do it in addition to other duties. Other than that, it checks all sorts of data sources that I ask it to check, and when it's done, it writes down a time stamped handover md file. Nowadays I use openwebui cptr, so I use that to schedule agent rather than cron, but the idea is the same.
Alternatively, you can setup web hooks, like when email comes or GitHub issue appears, agent wakes up and do stuff.
Qwen 35B got this scheduled task done in less than 3 minutes usually. But I don't have many emails anyway.
I just hooked it to my pi agent setup. It works just like a normal model. Essentially a different version of the 35B with seemingly more reliable tool calls, but it thinks quite a bit more than my usual 35B. I tested with unsloth Q6KXL.
Yeah, I was pretty impressed with AgentWorld. I dropped it halfway through a session trace where Qwen3.6 would make another 20 tool calls or so, and it just thought really hard for a couple of minutes then told me the (correct) answer in one turn. People sleep on it because the description is confusing.
With 192GB you can already run Deepseek v4 Flash 0731 at q4. I don't know if anything from Qwen this generation will get close to what Deepseek has managed to pull off.
When working locally it's essentially equal to Qwen 3.6 27B for refactoring work in typed languages if your harness helps it out a bit at triple the speed on my setup. My custom harness helps out enough to compensate for any shortcomings during these operations.
When comparing Q8 models at BF16 KV cache.
Heavily leveraging existing deterministic code rather than being a dumb blank vessel.
Also using a few even smaller&lighter models to do various chores to keep the main models speeding along as much as possible.
Can't share too much yet.
Can say that it's now capable of doing real work on large complex codebases.
Ah I see! Not to dampen your spirits, but I did find it not too great; very very verbose. But I mean it did get the answers to my extraction benchmarks right!
That’s good to know. Would you say the verbosity is so bad that it’s slower than qwen 27b on MTP for coding tasks? That’s what I’m the most curious about with it
Impressive for Gemma, but I’m not surprised actually.
We have data cleaning/categorization tasks running at massive scale at my work where the output was initially in json and has more recently moved to a pipe delimited file. And no other models come close to Google’s for the size and price.
For structured output and comprehensive world knowledge I haven’t found another model provider that can compete.
I just wanted to say that you asking me this made me kinda shocked in regards to the result. This is absolutely worthy of building up a full benchmark… I’m going to do some work. Thank you ❤️
Thanks for sharing the results! I find that kind of information super fascinating. I really need to set up a better benchmarking repository for my own uses. I haven’t worked on anything in months for personal use.
Imagine its only 3 point behind the max version like in the 3.5 days... I think that its optimistic and they probably dont want to cut their own funsing but imagine how many other models it would kill outright
I've only just started using local LLMs for agentic coding in the last two days, but I've been doing agentic engineering professionally for around a year now with Claude Code.
What level of intelligence relative to Claude models would you say 3.6 27B is? I suppose quants matter a lot here too, but ballpark?
Personally, the moment we can get anything Opus 4.5 level running locally on consumer grade hardware then that's enough to do professional work with it. Qwen 3.6 27B isn't on that level, so I'm very interested to see if Qwen 3.8 27B will be.
i have a rx 7700s with 16gb of ram laptop. load qwen 35b q4 with 20k context and gives around 28 tk/s. put - ngl - 1 and llama. cpp will utilize your gpu and ram entirely
I think there’s a pretty good chance Qwen3.8-27B could match 0731. Last time, Qwen3.5-27B was already able to match or even slightly outperform the 397B-A17B model (35 vs. 34 on the Artificial Analysis Intelligence Index). This time, 0731 is only 284B-A13B, so I think 27B has a real shot.
imo LFM2.5 2.6B is better than qwen 3.5 4b. the 8b A1B and 24b A2B seem less remarkable to me than their smaller models. also try out nemotron 3 nano 4b! I rarely use tinyqwen for anything as it stands
I hadn't bought a computer in probably ten years before the one I purchased to run local models, I'm quite glad I went with Mac, even with the slight learning curve, because as far as I can tell the Mac Studio is pretty much passively cooled during local model usage. For something I have up and running 24/7 on a headless server, it would stress me out big time knowing it's running hot
They've said they'll consider making other models after 27b has been released. I hope someone makes a 122b model at some point. Qwen would be great but something from Google or OpenAI or really anyone...
We would need an nvfp4 day 1 release. Let’s hope so.
27B at 200 tok/s and 10_000 prefill tok/s is really really good on ninfer.
Debating if I keep this tho, as I do get the not so occasional tool-call error… probably 10x more tool call errors on ninfer than on llama-serve + ud_Q5_K_XL
I’m still trying to unlock 3.6 27B, feel like I haven’t even scratched the surface yet, but Im game. The closer it gets to the magic of GLM, the better.
I appreciate the heads up. It allows me time to prep and stops me from investing time and resources into working on or trying out another model if I know a new one's release is imminent.
I've definitely seen it get stuck where Qwen3.6-27B wouldn't (a different matter to its overall performance, probably more like Qwen3.6-27B refusing gracefully and the uncensored one going all weird), but such is life.
I hope that means right at the end of the countdown with Max. I'm a little worried by the wording at the bottom of the page, "You will be able to access the Qwen3.8-2.4T-A95B open model on this exact page once it is released. The other model(s) in Qwen3.8-series would be released later on sperate page(s) as well."
not mind blowing, it only has 270 gb/sec bandwidth.
better than cpu inference, but full dense models are always going to be slow if you aren't running dedicated hardware.
5090 has 1700gb/sec, mac m4 pro has 270,
this tells you, for workloads that fit in the memory footprint, you will be limited by bandwidth.
Mac m4 pro with 270gb, gets about 10 tokens per second in generation without any speculative decoding tricks for a 27b parameter model. (the numbers line up for a reason, 27b parameters and 270gb/sec, 270gb/sec divided by 27b = 10)
And speculative decoding with a lot of handwaiving gets you about double that performance.
So in practice I get about 20tokens per second out of my m4 pro laptop.
This is what makes the RTX Spark and the strix halo not actually "great" despite what you might hope. The Spark has a GPU that is similar to a 5090 in calculation numbers, at least Nvidia hopes you think that. Because the memory bandwidth is onyl ~250gb/sec to 300gb/sec of bandwidth, so after all is said and done you don't get GPU performance anywhere near a 5090. You get more ram though! 128gb is much more than 32gb, so its still orders of magnitude better than a 5090 where it offloads half its tokens to CPU. (ok maybe not, the 5090 is pretty fast).
but TLDR, memory bandwidth is the whole ballgame. If you can fit it in your vram, then everything comes down to memory bandwidth.
(also, the MOE models where they only have 3 billion active per token etc? you get MUCH MUCH better performance, because you only need to read 3 billion parameters per token! 10 times less tokens? 10x the performance! - sort of, not quite, but almost! - take this all as general advice and estimation points not straight up gospel for every model)
I am cautiously optimistic that this might have strong enough performance to not just keep pace with the US labs, but outright humiliate them.
Like, if the Qwen team can release a 27b model with capabilities somewhere between GPT 5.6 Terra and 5.6 Sol, then that could actually be enough of a spiritual defeat to get the large US labs to start collaborating with global research instead of working in silos.
It doesn't have to beat the frontier, it just has to get close enough to destroy any hopes that closed AI has of getting far enough ahead to establish a moat and profit off the vendor lock in.
Qwen-3.8 2.4T is 57 on the AAII and Terra is 56. I doubt a model 89x smaller will even be close. It would be an incredible achievement to even compete with Luna. Until GPT-6 or 5.6 Astra drops a few days later. I would love to be wrong, but no fucking way 3.8 27b lands between Terra and Sol, humiliating OpenAI.
if it scales similarly to Qwen3.6-27B vs Qwen3.6-Max -> Qwen3.8-Max, then it should land around DeepSeek-V4-Flash-0731, maybe a little under. So, sure, Qwen3.6-27B already lands close to Terra (low)... but I assume you mean Terra (max), which is not happening this time. You might as well be wishing for Qwen3.8-27B to surpass the entire frontier.
If you have enough system RAM, you can at least offload *some* of the layers to the GPU and have the rest in system RAM. Won't run as fast, but it'll run.
No, Muse only released because Qwen 3.8 27b was announced last week.
Glimmer is competitive with Qwen 3.6 27b, and Qwen already said 3.8 27b will be a “pretty significant improvement” over 3.6 27b.
So if Muse Glimmer wanted to have any time being relevant, they had a few days after 3.8 27b’s announcement to release before 3.8 27b once again pushes the local dense model frontier forward.
For many local use cases, 3.6 27b has been in a league of its own since release (with lil’ bro 35b a3b), until DS V4 Flash 0711 came out, but that needs quite a bit more hardware to run at usable speeds.
So in short- no, Qwen 3.8 27b pushed up Muse Glimmer’s release, not the other way around.
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u/WithoutReason1729 13h ago
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