If it’s not zhipu then why is it returning errors that zhipu does for other models? Who else would return the exact same errors even if they took a lot of core infra like tokenizer from z?

I wonder if the NCD metric says something about distillation too. Would you expect that a model that has been distilled/seen traces from other models would have a smaller NCD? It would be really interesting to see if this holds up and provides evidence of distillation or certainly evidence of model outputs being used in the training mix.

As someone who uses NCD nearly every day, I have concerns about how it’s been used here.

But while we’re “guessing”: Xiaomi MiMO

It's not a good model tbh, got a bunch of things wrong that Opus corrected in my codebase.

GLM 5.3 and all previous models don't have a vision encoder and can only accept text. Ox-Alpha can accept video and images, so unless Z-ai added a pretty good vision encoder for this model, I don't think so.

My money is on Moonshot and this being Kimi K3.5. The measured tps and latency is in-line with K3's tps and latency from Moonshot.

MiniMax M3.5 is also possible (but the MiniiMax provider is a lot more performant than the lab behind ox-alpha, so less likely).

Dont rule out ssi

I think within 12 months we’re going to see a frontier (inc open models) that’s so good at almost all human-directed tasks that which model you use just won’t matter. Only differences that remain will be in deep research or very long-range tasks.

Related:

Ox Alpha

https://news.ycombinator.com/item?id=49381896

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