Qwen3.8-2.4T

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 3.8-max shows a lot of improvement. reply: 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. Asking because in my case (OCR of scanned historical "National Geographic" magazines) the LLM trying to merge text split into separate columns was running in circles from time to time and needed a lot of prompt tuning when using Qwen 3.0/3.5/3.6 (still needs from time to time).

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... reply: 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.

https://unsloth.ai/docs/models/qwen3.8The 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. reply: 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....

> 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... reply: 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_...[2] https://huggingface.co/baseten/GLM-5.2-Vision-NVFP4

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... reply: Interestingly, the high variant does a lot worse and failed to generate a valid SVG (and the low variant use more tokens than the high one, so maybe their reasoning efforts are not working properly).The solar system animation is also the coolest looking I've seen, unfortunately the animation doesn't work:https://aibenchy.com/compare/qwen-qwen3-8-2-4t-a95b-low/qwen...

I'll just fire that up on my Intel n100...

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? reply: OpenRouter is (roughly) a single proxy between you + many different models + providers. it works with opencode (+ many other products), and is relatively convenient for trying out a bunch of models.for example, they already have qwen3.8-maxhttps://openrouter.ai/discover?model=qwen/qwen3.8-maxnote that they add some fee ontop of things (maybe 10% of spend?). it isn't htat big of a deal for general experimentation, but if you end up wanting to use a single model in a higher-volume way, it likely makes sense to cut them out of your stack.

More curious about how qwen3.8-27B performs. That's the size that I can run locally.

Is this the largest ever open weight model release by parameter count? I think it is.

Do we know if AA and DeepSWE benchmarks are on bf16 or fp8 quantisations?

when will we see MIT license Qwen again?

The card looks almost too good to be true

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.

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.

the a little disappointing part is this is released in BF16. so i suppose no QAT was implemented.

"QwenSVGBench" elo 1713, pelicanmaxxxing confirmed?