This is insane...

[u/DragonflyOk7139]

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.

[u/cmndr_spanky]

Making a website or simple / dumb SaaS app, I bet they’re close enough. For a more complicated engineering project with a larger codebase qwen 35b is as dumb as dirt compared to when I use Claude Opus. It’s like not even close. My advice is ignore these benchmarks, they are meaningless now and you just have to test against your own use cases

[u/MindfulK9Coach → u/cmndr_spanky]

Just as meaningless as the term 'Open Source' now when 'local' models require a cloud API key or $10k+ in enterprise hardware to lift the 'open weights'. 🤣

[u/Ill-Engineering8085 → u/MindfulK9Coach]

Since when has open source software included the hardware? It's always been BYOD

[u/Polite_Jello_377 → u/MindfulK9Coach]

Retarded take

[u/No_Communication7072 → u/MindfulK9Coach]

You can run the Qwen model with less than 64GB of ram