▲ 711 points
back
170 comments
Supposedly this is a Kimi k3 rival. Bit of a chonker, especially since they only released bf16 and fp8. So at launch this will be harder to serve than k3. No QAT on q4 means that someone with deep pockets (nvda?) will have to quant it, with plenty of calibration data. Should bring it ~1.3TB, so around k3 size.
License pretty similar to k3 with some caveats. Free to use for internal or <50M$ revenue / year. Limitations above that threshold for serving the model or services targeting coding / productivity agents.
Benchmarks are looking good, trading blows w/ opus4.8 and sol, generally 10-20p under fable. But that's neither here nor there w/ qwen, their benchmark to real world usage correlation has been iffy in the past.
The local model 3.8-27B announced for Friday, same time so ~48 hours from now. That'll be a bit more exciting for a lot more people, since 3.6 was quite good for local inference, and their 3.7-max -> 3.8-max shows a lot of improvement.
From my evaluation[1] neither Kimi K3 nor Qwen 3.8 are as good as GLM 5.2 at coding. I wonder if there's a marketing gap that's got people underestimate it.
It uses twice as much tokens to achieve the same but the results are significantly better and because it's so much cheaper it's the most economical choice too.
[1] https://blog.bosun.ai/software-maintenance-with-open-weight-...
Unsloth already has a guide for their quants: https://unsloth.ai/docs/models/qwen3.8
The parameter "reasoning_effort" is something new, or am I wrong?
Qwen3.8 comes with official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:
- xhigh (default): for complex tasks demanding thorough analysis
- medium: balancing accuracy and speed
- low: efficient reasoning optimizing for speed and cost
In addition, preserve_thinking is enabled by default for all workloads for the best out-of-the-box experience.
> 3.8-27B announced for Friday
Maybe I’m misreading this or some other post, I thought QWEN was stepping away from releasing these models for local consumption
Now that they have reached the frontier in raw performance, I would like to see Chinese models improve their reasoning efficiency.
Llama.cpp can quantize without special training, but I'm not sure if any special model architecture support is needed to read it in the first place. If it can be converted to gguf at all and you know what tensors to target, it can get the full ternary bonsai treatment today.
Show me any open source model quantisite, distile or reduce size from nvidia ;)
quanting is actually cheap and you can compress a model that does not fit on a GPU. You can process layer by layer, this is what the sequential processor in llm-compressor does.
Also of interest: DeepSeek V4-Pro-0813 (1.6T-A49B) benchmark scores have apparently just been announced on the DeepSeek WeChat channel and they're sitting about Fable 5 level.[1]
[1] https://www.reddit.com/r/LocalLLaMA/comments/1vmi0fg/deepsee...
Isn't this quite a bit behind Sol and Fable and even ChatGPT 5.5 xhigh and Opus 5 max?
In terms of what you get for what you pay for, it's incredible - probably by far the best.
But unless I'm reading things wrong, it does not appear to be top-of-the-line.
And relatedly, just now available on OpenRouter
Does that mean they're using the new pricing now? I no longer see the warning/notice about "Things are about to get a lot more expensive soon" on https://platform.deepseek.com/usage anymore, so I guess yes?
ive not paid any attention to this space much, is deepseek referring to paid-sub service like claude/gemini/etc ? or is this local llm
https://unsloth.ai/docs/models/qwen3.8
The 1bit quant model is at an astonishing 397GB with 95B active per MOE. This literally puts Opus 4.5 performance level into a machine a normal person could buy, and still gets usable tokens/second.
The full lossless model BF16 is clocking at 4.9TB. The model card claims the model to be between Opus 4.8 and Fable 5. Again that's astonishing as getting a machine with 7TB RAM (with context + KV cache) is still within the realm of medium size companies.
Bad things: The open source version has its vision capability removed, and the context capped at 250k . I expect someone to bolt a Kimi 2.6 vision tower to it to restore the vision capability (at less performance of course). For context, I played around with extending the context to 600k for Qwen 3.5 397b, and the context remained stable up to around 480k. It'd be interesting to see if the same can be done to Q3.8 .
Also no out of the box DSpark/DFlash support. MTP is present so we should at least get some boost in TP speed.
To compare a 1 bit quant to the full fat model is misleading.
Honestly this model people at home can tinker with, if you have a big enough Mac. Maybe 4 Strix Halo/DGX Spark, and then at 1 bit quant? Nah.
Use the right sized model, for your hardware. You'll get better results.
I don't understand the logic behind model sizes and quantization.
Suppose I have 100GB of unified memory, how should I know which model suits it best? I understand how a 2.4T model wouldn't fit, but I don't understand the impact of quantization and whether I should use a 200G model quantised to fit say 90GB of memory, or a non-quantised 90G model.
Opus 4.5 level of performance is also accessible with deepseek-v4-flash-0731 (0731 being the july 31 update) which is much, much, much smaller. 2x RTX pro 6000 blackwell can run it. 4x can run it very comfortably
Opus 4.5, even 4.6-level performance has been around since July 31st in 284B total params and just 160GB of weights at native FP4 quantization- DSv4 Flash.
> The 1bit quant model i
at this kind of quantization is it useful though?
> In particular, Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B with more features, such as vision input & non-thinking support, 1M context length by default, official built-in tools, etc.
That is unfortunate, that the open weight model doesn't have vision support or the 1M context length...
People have had surprising success adding vision to open-weight LLMs that ship without it, like DSV4 Flash [1] or GLM-5.2 [2]. Given this model is already vision-trained I expect that approach will work well here.
[1] https://old.reddit.com/r/LocalLLaMA/comments/1vl6ior/i_gave_...
Qwen3.5 was awesome: fairly open and fully featured. 3.8 lacking vision, nerfing thinking modes, and low context length feels pointless.
More curious about how qwen3.8-27B performs. That's the size that I can run locally.
Yeah, I must have misread the press release last week as I thought it would be released at the same time.
Shall we bet on when the hardware needed for this (without quantizing and at good speed) will reach < 10k USD? I'm betting 2040. I can download it now, and then get the hardware later. Eventually we can all have these things running 24/7 in our home if we wanted to. I currently would not have any task for it that would really utilize the hardware 24/7, but maybe in 20 years I will.
I think it is more likely that a smaller model (<400B) with similar intelligence gets developed long before the hardware to serve a 2.4T model gets cheaper than 10k.
I would say 5 years.
The whole industry is now pushing through memory.
In 5 years you have either some type of explosion which willjust make all the hardware from today affordable or you have such an AI explosion, that the today hardware is written off and not efficient enough anymore that you can buy it for cheap.
In parallel, its clear that we need more memory.
In parallel models in hardware will become a thing on mass market.
In parallel everything gets more efficient. The 30B parameter model will be for sure more intelligent in 5 years than it is today.
approximately $20 million for 750TB unified memory custom interconnect right now
Not seeing the upside versus K3 here, especially with the intentional capability loss.
Read the room, Qwen. It's not a good time to hobble your releases.
I'll just fire that up on my Intel n100...
Vocabulary size ~248k. A bit bigger than other recent Chinese models (Kimi K3 ~164k, DeepSeek-V4 ~129k, and GLM-5.2 ~155k).
Make of this what you will.
> Make of this what you will.
I'm interested in your take on it. IIRC Gemma family models too have a ~250k vocabulary size
I've been wanting to run open weight models lately to give them a shot with OpenCode. However, I get the impression that models like Qwen and Kimi k3 are impossible to run locally? I have a RTX 5090 and 64 GB of RAM but the models seem to be much larger than that. What's the route to start using these models? Bedrock?
That's a really cool hamster [0], unfortunately it's really expensive now, 2x more expensive than Grok 4.6[1].
[0]: https://aibenchy.com/compare/x-ai-grok-4-6-high/bytedance-se...
[1]: https://aibenchy.com/compare/x-ai-grok-4-6-high/bytedance-se...
Is this the largest ever open weight model release by parameter count? I think it is.
when will we see MIT license Qwen again?
The card looks almost too good to be true
Has anyone compared the programming capabilities of Qwe3.8 and Kimi K3? Which one is better?
People online say this model's performance isn't very good; what do you think of it after using it?
Do we know if AA and DeepSWE benchmarks are on bf16 or fp8 quantisations?
A ~5TB model.
why is this page suddenly 404? Is Alibaba going back on their words?
https://modelscope.cn/models/Qwen/Qwen3.8-27B
3.8-27B LETS GO!
best crypto-bro impression I can do...
the a little disappointing part is this is released in BF16. so i suppose no QAT was implemented.
"QwenSVGBench" elo 1713, pelicanmaxxxing confirmed?