This is pretty impressive and a bit like how the GPT-OSS-120B came out and scored pretty well on the benchmarks despite its somewhat limited size.
That said, using LLMs for software dev use cases, I wouldn't call 256K tokens "ultra-long" context, I regularly go over 100K when working on tasks with bigger scope, e.g.:
Look at the existing code related to this functionality and the existing design patterns in the code as well as the guidelines.
Then plan out the implementation in detail and ask me a few questions along the way to figure the details out better.
Finally, based on everything so far, do the actual implementation.
Then look it over and tell me if anything has been missed from the plan, then refactor the code in any number of ways.
My current setup of running Qwen3 Coder 480B on Cerebras bumps into the 131K token limit. If not for the inference speed there (seriously great) and good enough model quality, I'd probably look more in the direction of Gemini or Claude again.
This stuff can run on a local machine without internet access, correct?
And it can pretty much match Nano Banana? https://github.com/PicoTrex/Awesome-Nano-Banana-images/blob/...
Also -- what are the specs for a machine to run it (even if slowly!)
This has nothing to do with nano banana, or image generation. For that you want the qwen image edit[1] models.
the model discussed here is text model, so similar to ChatGPT. You can also run it on your local machine, but not yet, as apps need to be updated with Qwen 3 Next support (llama.cpp, Ollama, etc)
Yes.
> And it can pretty much match Nano Banana?
No, Qwen3-Next is not a multimodal model, it has no image generation function.
Make sure to lurk on r/LocalLlama.
Please do take everything you read there with a bit of salt though, as the "hive-mind" effect is huge there, even when compared to other subreddits.
I'm guessing the huge influx of money + reputations on the line + a high traffic community is ripe for both hive-minding + influence campaigns.
What will the actual next advanced release be called:
* next-next
* next (2)
* actual-next-final
I'm skeptical about these claims. How can this be? Wouldn't there be massive loss of world knowledge? I'm particularly skeptical because a recent trend in Q2 2025 has been benchmaxxing.
More efficient architecture.
> Wouldn't there be massive loss of world knowledge?
If you assume equally efficient architecture and no other salient differences, yes, that’s what you’d expect from a smaller model.
I recommend playing with the free hosted models to draw your own conclusions: https://chat.qwen.ai/