And to read the tea leaves a little:

3.8 actually performs slightly worse than 3.6 on AA-Omniscience Accuracy, which could imply that they traded out world knowledge for capability in other areas.

It also produces nearly twice as many tokens per task as 3.6 (and by extension, time), which may be a tradeoff required to achieve correctness at this parameter size.

And more context:

Same score as the latest DeepSeek Flash 0731 which has 284B parameters! (13B active)

Its also the second best Qwen model, much better than Qwen 3.7 Max, but significantly below Qwen 3.8 Max.

Also with Qwen 3.8 being more token hungry than Luna, using around 2.3x tokens. Which hurts for local deployment.

We don't actually know how large they are, actually.

Which should tell you how useful these benchmarks are.