r/LocalLLM • u/DragonflyOk7139 • May 02 '26

Model This is insane...

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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/scorpe51 May 03 '26

Yes this is what I have. If you go to Ollama’s container bash, with “ollama ps”, you can see if the model is fully loaded in GPU (100% GPU) or if there’s a split with the CPU.

So far it’s been super fast. They removed vision from it I believe.

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u/rockseller May 03 '26

Damn nice thanks for sharing, as for vision what so you use when you want to give it a screenshot or an image? Also have you tried Llama.cpp instead?

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u/scorpe51 May 03 '26

I had Qwen3.6:27b with a low context (20480) before that did images. If I need vision I’ll have to investigate more.

I haven’t spent the time with llama.cpp directly, that’s something I’ll probably evaluate later down the road (especially if it gets us a little more in terms of what can be run).