anana_

The benchmarks they chose are rather cherry picked to not include long context or difficult ones that involve long horizon work or many agent turns, as I suspect this is where the model shows more differences compared to the full fat one

I'm curious what the actual rate of replacement/useful life of these GPUs running AI inference 24/7 is.

If these cards burn out in less than the ~5 years of depreciation that accounting puts them at, well then there will be problems.

Agreed. Luckily, this model also scores high in AA non-hallucination, so it knows what it doesn't know -- perfect for situations where it can just tool call a web search.

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.

For more context, this puts it on par with models like GLM 5.2 and GPT 5.6 Luna, which are far larger

Seems like MTP is available immediately!

As was the case with GLM 5.3, it seems that there is still much juice to be squeezed from post-training

Monstrous benchmarks! Hoping it is not benchmaxxed.

What a week for AI model releases

They keep mentioning a 31B dense model, but there are no benchmarks or weights for it anywhere?

It looks like the purpose of this model is to i. generate environmental sim data for doing RL on other models or ii. act as a foundation model (they trained it to select actions as well as predicting the next state in the same loop?)

Either way, neither are intended for end consumers.

I believe the benchmark listed is about simulating the environment for the various tasks, rather than doing them. It seems that the point of this model is to generate sim data to improve other models with