There's a lot of brand confusion among the Chinese models right now. Kimi, Qwen, GLM, Z.ai, Ox. We might know the difference (or I should say, someone does because I'm losing track already) but these models have no chance at end user penetration and loyalty until there's a single focused survivor.
It took me a year talking about it until my wife knew that ChatGPT and Gemini are two different things.
I disagree. I think most developers who are savvy enough to be using openweight models and/or running models locally are not dealing with the same level of confusion you are.
Ox is just GLM. And z.ai is the maker of GLM.
The main players in the openweight model market have been known for a while.
And they already have significant user penetration.
That benchmark is super sus. Until someone pointed it out, the top performing open weights model was a Kimi K3 fine tune from their sponsor (abacusai/Smaug-Agentic). Now, it's not on the list.
I really want to see hard evidence of distillation before I buy into it. Seems like a lot of sour grapes over not having the sort of lead assumed. In this field, it has been shown repeatedly that leaps in performance come swiftly and without notice.
Even if it weren't slopped together, 65% vs 80% on 10 tasks just isn't a significant difference. For 80% power to distinguish at a significance level of 0.05, you'd need more like 140 samples, if those were the true success probabilities.
The number one problem in LLM benchmarking is that people try to draw conclusions from sample sizes far too small to conclude anything but "it works sometimes, it fails sometimes, hard to say which is better." (The number two problem is that people run benchmarks blindly without checking that they measure something meaningful.)
I'd be interested to know what was going on with it during the public test as there were numerous reports of it improving considerably at tasks it was asked to do early on in the test compared to later in it.
It's logical to serve the best version (quant) of the model at the beginning so that users keep testing it. It is also reasonable to think that the developer of the model tried to test various quant levels by gradually degrading the model's capabilities.
1. Just variance in pass@K. If you prompt any model multiple times you'll see a large variance. N=1, but I find chinese open source models have a higher variance than higher-RL'd models like fable/opus.
2. They legitimately shipped a new RL checkpoint over the 7 days, which I find hard to believe.
> The company on Wednesday confirmed speculation that the Ox Alpha model is a new iteration of its GLM series and said it will release the weights for it tonight, in response to queries by Bloomberg News.
Seems legit.
It's really hard to know how good it is. So much hype around it.
completely vibes based, but ive been using it to port Mindustry game from Java to C# with agents, and its been working for 50 hours (its 15-20 tks so super slow inference). Its done a fantastic work and its almost finished now. Better results than deepseek flash and gpt luna by a mile on this kind of long term work. Less good than gpt sol or opus. We dont know the param count but my guess is 200-300 range.
likely a distilled glm 5.3 that will punch within 20% of that at 2-3x less size. you'll find that capability is typically very jagged on models that are distilled
Only reason people are interested is it’s free at the moment. I wasn’t impressed by its performance. Once the model gets a price tag it’s usage will be negligible.
It took me a year talking about it until my wife knew that ChatGPT and Gemini are two different things.
Ox is just GLM. And z.ai is the maker of GLM.
The main players in the openweight model market have been known for a while.
And they already have significant user penetration.
https://livebench.ai/
while here it outperforms Fable by a significant margin:
https://oxalpha.com/
but if the latter is true, will people still say it was "distilled" from Fable?
Source: https://twitterwebviewer.com/?tweet=2091116504787935350
The number one problem in LLM benchmarking is that people try to draw conclusions from sample sizes far too small to conclude anything but "it works sometimes, it fails sometimes, hard to say which is better." (The number two problem is that people run benchmarks blindly without checking that they measure something meaningful.)
Many people and even software engineers fall for this all the time.
Most of these people are from crypto pivoting to AI doing this.
AI has made this easier and cheaper and it is going to get a LOT worse.
Imagine lots of websites with typosquatting and looking exactly the same as another website, vibe coded and cloned within seconds.
The public have no chance.
1. Just variance in pass@K. If you prompt any model multiple times you'll see a large variance. N=1, but I find chinese open source models have a higher variance than higher-RL'd models like fable/opus.
2. They legitimately shipped a new RL checkpoint over the 7 days, which I find hard to believe.
I am leaning towards 1.
Seems legit.
It's really hard to know how good it is. So much hype around it.
on toy benches it made quite a few mistakes but was able to fix all of them on its own
(meaning more tokens, more turns, more tool calls — but same outcome as gpt 5.6 sol)