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I don't agree with that conclusion. I'm looking at the benchmarks page for LFM2.5-24B-A2B, and it appears to get a worse score on SWE-Bench-Verified (by a lot) when compared to Qwen 3.6 35B-A3B. It only clearly beats Qwen 3.6 35B on AutomationBench, ToolSandbox, and LiveClaw.

At least for my use cases, Qwen 3.6 35B-A3B appears better than this new release. An MoE for Qwen 3.8 would be even better than that!

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Would you call Qwen 3.6 35B "similar size" with ~50% more parameters (both active and total) though? I believe what GP meant that if there was a Qwen 3.8 24B-A2B, it would roughly be as intelligent as this one.

Smallest Qwen Q3 quant is the same size as LFM 25B Q4 ≈ 14 GB, but much better at coding and following my instructions.

Way more excited for Nvidia's A9B model coming out tomorrow.

Why? Their models are useful as a starting place for doing your own fine tuning, but they have never stood well on their own.

they always undertrain the model

yeah it's kind of the point. they're pedagogical tools. they give you the full recipe for building it from scratch or fine-tuning it beyond what it is.

but i'm prepared to be surprised, maybe they'll conjure up something standalone useful this time

They have an incentive to produce a more finished model to go with the launch of the RTX Spark laptops, I guess.

Not really - you pay for the hardware. Everyone else already is building models for Spark hardware.

I think their entire pitch about local AI is best demonstrated with their own model that they can optimise for it, and the shape of this model seems optimised for the smaller RTX chips. Indeed, this model appears to be part of their launch picture:

https://blogs.nvidia.com/blog/local-ai-rtx-spark-laptops-sof...

Well, weights released:

https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-1...

And their own benchmarks seem to show it well behind of Qwen 3.8 flash next on pretty much everything except generation speed.

That's not the model onlyrealcuzzo is talking about. He is talking about the A9B that was just released five or six minutes ago.

Which is not going to compare well to the 3.8 Flash because it's much smaller; designed to fit quite a large model into quite a small space.

Will be interesting to see how it compares to Muse Glimmer, Qwen 3.8 27B and the older Qwen 3.6 35B (and how its image analysis performance compares to Gemma 4 12B QAT, which is still IMO the most interesting open weights model released all year).

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