Ox Alpha is almost certainly a model by Z.ai.

https://files.catbox.moe/k52n6k.png

The upper chart shows the availability of Ox Alpha and the lower chart shows the availability of GLM 5.3 by Z.ai. They had a blip at exactly the same time.

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).

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?

    > 1 quadrillion tokens per day on Nous portal

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 is probably from Google and is probably hosted on Vertex AI. Opencode announced that responses from Ox Alpha should be better and soon posted about Vertex eu and us multi region update in their changelog. I could be a Gemini model or a new one based on GLM based on tokenizer. Also the amount of inference it is providing for free is something only google can support with its TPUs. So maybe a GLM based model running on TPUs.

My bet: it's Google running a "new" model based on GLM

Yes: https://news.ycombinator.com/item?id=49446422

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.

Related:

Ox Alpha

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

The Ox-Alpha webpage really make it sound like they are trying to hype a model that has nothing particular to show:

"The reasoning model that appeared out of nowhere. Built for code, long-horizon agents, and a million tokens of context. Nobody knows who made it — everyone wants to try it."

> How many words are in the previous message?

Its amazing to me that providers haven't added any sort of masking of the prompt in the thinking traces to avoid prompt extraction via this sort of trivial attack

As far as I'm concerned, GLM is the opengl mathematics library.

I wish projects would check before stealing a name.

My strong prediction: this model is around 64B and can run on laptops. Thats the reason behind the hype.

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

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.

Dont rule out ssi

Nvidia