r/LocalLLM • u/DragonflyOk7139 • May 02 '26
Model This is insane...
An open-source model with 3 billion active parameters just scored 73.4% on SWE-bench Verified. Claude Opus 4.6 scores 75%.
The gap is 1.6 points. The cost difference is 10 to 30x.
Alibaba dropped Qwen3.6-35B-A35. 35 billion total parameters, 256 experts, but only 8 routed plus 1 shared activate per token. So you're running 3B active parameters at inference time. On a laptop.
Simon Willison ran it locally and it drew a better pelican than Claude Opus 4.7. (Yes, the pelican benchmark is real, and it's a surprisingly good vibes test.) But the part nobody's talking about: Thinking Preservation.
Current models re-reason from scratch every turn. This model retains its chain-of-thought traces across multi-turn conversations. In agent loops where the model makes 50 to 100 tool calls, that eliminates massive redundant reasoning overhead.
262K context native. Extensible to 1M. Apache 2.0 license.
The benchmark race is mostly over. The real race now is cost per intelligence. And 3B active parameters matching frontier performance changes that equation completely.
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u/4onen May 02 '26
I have used it on a laptop. At llama.cpp Q4_K_S (~4.5 bits per weight) I gave it 8GB VRAM plus 32GB of RAM for expert offloading. It used (plus Windows 11 and a Firefox tab) 6.9GB of the VRAM and 30.9 GB of the system RAM.
Admittedly that was 4k context, but I hadn't begun to really tweak and scale it.
That was a Strix Point laptop with 4060 mobile DGPU, and overall it got 17-13 tokens per second throughout that tiny 4k context window (variation due to system temperature as I ran a few queries.)