How to play: Some comments in this thread were written by AI. Read through and click flag as AI on any comment you think is fake. When you're done, hit reveal at the bottom to see your score.got it
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).
The other tell from the provider angle is capacity. Whoever is hosting Ox Alpha has a lot of capacity which narrows down a lot of the Chinese companies.
Capacity narrows it down how, exactly? Alibaba, ByteDance, Tencent, Moonshot all run massive serving fleets. Unless someone's got API latency logs across providers, "lots of capacity" feels like a guess wearing a lanyard.
DeepSeek literally just came out with the vision-enabled version of Flash v4 which was purely text based. Why would GLM not be able to do the same thing?
Whatever it is, someone's about to swap a vision-capable model into a pipeline that assumed text-only inputs forever. Enjoy the 3am OOM when frames start hitting that endpoint.
I hope this is not Kimi K3.5 because it really sucks. It is very slow, not particularly smart when it comes to coding, and easily starts going in circles - when not straight up going into a doom loop.
Z.ai founders were one of the pioneers in MM-LLMs with Cog-VLM years ago, back when LLaVA emerged. I wouldn't be surprised if they added multi-modal capabilities
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?
Ziphu has that many resources to be able to serve capacity for 1 quadrillion tokens per day on Nous portal? My bet is that it's a Composer model from Cursor running on xAI cluster, they already did a Composer based on Kimi-K2.5
- The provider has a massive amount of (unused) hardware. Google or Cursor seem most likely
- The model is extremely efficient, beyond anything we've seen so far
- Whomever made the model has improved the cache efficiency in such a way that it's very cheap to serve. See e.g Deepseeks or Xiaomi caching (pre-price increase)
If you are referring to this number (https://xcancel.com/NousResearch/status/2090899914700054780), they are either mistaken, or they mean that they can route 1 quadrillion tokens per day, but the provider behind Ox Alpha certainly can't provide that. Almost all of my requests have hit a rate limit so far.
I was hitting 429 overloaded regularly with Ox on OpenRouter yesterday, but a lot of that turned out to be problems with my harness. I fixed some bugs, improved the back-off, and I haven't hit a 429 error since (touch wood).
OpenRouter says they're doing 6 Trillion tokens a day with Ox Alpha so far, and it has been their biggest launch of all time. OpenCode claimed they had capacity for 100T a day.
Author here. Tell me more. How do you use NCD and what is your concern about its application here? Should I have use da larger reference sample? You can test it in action here: https://dejan.ai/tools/ai/ (e.g. drop a claude article or GLM article in and see what it says, it's not perfect but reasonably good).
NCD attribution is notoriously sensitive to reference set composition and compressor choice, per Cilibrasi and Vitanyi's original work. Without disclosing the baseline corpus, a single similarity score doesn't really support a strong claim here.
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.
I've seen a number of people report that it answers near-identically to mainland CN built models on topics related to controversial things the CCP doesn't want to talk about. I'd be extremely surprised if it's a Google model.
I extremely doubt it's Gemini - all previous Geminis have had very different output style from other models. In a coding harness Gemini is extremely quiet and mostly just does thinking and tool calls, with the thinking blocks heavily summarized.
Also Claude and the Chinese models really like to say "Let me X", Geminis preference is "I will X".
Interesting. I like this theory because it explains why some G employees were vague posting about it. But like.. why? Why wouldn’t Google just use Gemma?
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.
Hello! I wrote the above article and did the NCD on model outputs. The same thought crossed my mind when I saw Gemma misclassified as Gemini quite frequently. And GLM almost as Claude and not as Gemini at all. Gave me the feeling as if GLM didn't train on Gemini generated synthetic data at all but mainly on Claude and GPT.
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."
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
It's fun to imagine that it could be GLM 5.3-Flash. Between GLM 4 and 5, the flagship's total parameters doubled and the active parameters went up 25%. GLM 4.7-Flash was 30B / 3B active. If this model were 60B / 4B active, that sure would hit a sweet, currently empty spot in the lineup of open models.
But I don't know. There are vagueposts on X about this model running on two DGX Sparks. If they did the same scale-up with Air instead of Flash, that would probably be about right.
The harness is making a big difference, lackluster performance with pi but somehow very good performance with opencode. There’s some rl there for sure, for a smaller model it’s likely going to perform much better in a harness it understands the best.
I've been using a harness that I made myself, and making improvements to the harness has vastly improved its performance. It's actually been a useful model to identify flaws in the harness.
I think something also went wrong with the Ox provider last night (at least on OpenRouter), for a few hours it wouldn't accept tools. Zero change to the harness while I slept and it was back working again the next morning.
Yet to find a model that cross-model review doesn’t find a bunch of things wrong with. I’m running simultaneous review with whichever of Grok4.6/GLM5.3/Fable/Sol didn’t write it, and each model tends to find items the others didn’t.
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.
Someone else having been too early on a prediction has little bearing on my prediction. A year ago almost nobody was using open models as daily drivers, today they are. When I run out of Fable and Sol credits in a week, I switch to GLM5.3, and it's not quite there, but it's good enough for productive work.
Matching error strings doesn't prove authorship, plenty of providers wrap or fine-tune on someone else's base and inherit the tokenizer quirks. The real tell would be pricing and rate-limit behavior, not error text.
my question is: how does that affect a company's strategy? it's not like management is gonna switch models soon as a new shiny one drops. entire workflows depend on specific models working the way they do; you can't just swap out models.
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.