I couldn't get past all the network errors on OpenCode. Seemed smart enough, and was useful when I was low on usage on Claude, but beyond that, really hard for me to say whether it was Good or Bad.

Doom loops are as much of a model problem as it is deficiency of the harness. I have not seen any other open source harness that deals with them except the one I started because of this obvious gap.

See my other comment with examples where 0x Alpha is working non-stop on various projects with zero problems.

I noticed that this has a hard copyright rules...

I usually see doom loops when working with quants. Likely theyre trying to maximize the viability of a efficient model quant that can bw upgraded. Like cutting coke to get crack, quantiry over quality.

> One of them was running the same bash command about a thousand times.

An amusing thought of returning to your workstation to find it as an obsidian block after it gets stuck executing "dd" thousand times.

Were you using the full model or a quantized version, and what harness/configuration were you using?

It sounds like you were using a quant model.

thanks for pointing me out on Unwall.App!

Didnt know they exist - looks very good, maybe even better than Archive.ph

Claims about Ox Alpha performing at Fable level were from the social media hype cycle. Everything new in the LLM space brings a wave of influencers hyping it up as a revolutionary leap forward. Don’t forget to like and subscribe to learn more.

It is a capable small model, but it’s not frontier level. The interesting part will be seeing the model size, how it responds to quantization, and how fast it runs on the kind of non-server hardware that we can buy without selling a kidney.

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.

Source: https://twitterwebviewer.com/?tweet=2091116504787935350

the 2nd website is not official, just something someone slopped together for some reason.

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.

omp+0x-alpha beat both cc+fable and codex-sol in creating/refactoring a big eval setup. the former just knows where things should belong and completed the task all the way while the other two failed on both metrics.

Kinda useless to compare simply based on model without considering harness. Different agents handle the context etc completely differently. I would like to start seeing these model vs model comparisons across different harnesses.

GLM 5.3 was a great model, so this would be strange to release a regressed model

the outperform Fable was a mid (not completed) benchmark run. Real results were lower.

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.

Two potentials from my pov:

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.

lol don't shout out the obvious

I had good experience with GLM 5.3, but...

Z.AI is the only provider for GLM 5.3 on OpenRouter. I don't see 5.3 on Hugging Face. Not sure if this new model is "full GLM" or something smaller, or if they will like Moonshot AI publish weights but put restrictive license [1], which will again leave Z.AI as single GLM model provider on OpenRouter.

[1] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE

I happily ran it in Pi without issue.

Definitely agree. If it is small (eg. Qwen 3.8 28b or gpt-oss-120) then this might be amazing. If it is anywhere near Kimi K3 it would need to have some other differentiating factor than intelligence.

All smaller models and models behind frontier are expected to be released by default. Otherwise there’s no reason to produce them.

Chinese labs are not releasing all of their model weights. Qwen is known as an open weight model by most, but their top model is not open weight.

Releasing weights is a marketing strategy for newer labs to get their brand out there.

they are playing a completely different game than the US

> 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.

they have confirmed it officially

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

63% at DeepSWE.

https://x.com/davis7/status/2091285712566140986

Wenghi is behind DeepSWE, one of the best benchmarks.

> in which timezone?

Apparently someone working at a 3rd party inference provider also got confused and posted confirmation about it being a glm-flash model, despite having an embargo on that info. Someone jumped in the comments and told them they missed the timezone :)

In any case it should be releasing in a few hours. Timezones are hard.

Singapore I would assume. Z.ai usually peg everything to Singapore time.

China is GMT+8

> I'm enjoying working with Ox in a way that I'm just not enjoying talking to the 5.0 Anthropic models.

That’s very valid, but right now every other model I use is easier to talk to than Opus 5.0

Opus 5.0 has an impenetrable way of communicating. I can parse it, but it takes so much more work than it should.

> …and it's fun. I'm enjoying working with Ox in a way that I'm just not enjoying talking to the 5.0 Anthropic models

hard agree. it does not really feel "smart", but the personality is super refreshing

But they’re not at all a new player.

That is saying a lot if Ox Alpha is also small and relatively cheap computationally. I hope so; I love deepseek-v4-flash-0731 and use it frequently. Fast inference is good and fits with my dev style: I like to be in the loop, not let an agent code on its own for long periods of time.

It is not going to be cheaper though. I may choose the cheaper one in the end because performance will be marginal, both being flash.

There's a lot of brand confusion among the American models right now. ChatGPT, Claude, Gemma, OpenAI, Meta, Google, Muse Spark, Anthropic, Microsoft, Gemini. 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 Kimi K3 and GLM 5.3 are two different things.

I disagree. I think most users 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.

> have no chance at end user penetration and loyalty until there's a single focused survivor.

But why does that matter? End users (I believe, feel free to correct) do not really contribute all that much revenue-wise. They're certainly not the SOTA target audience.

The professional market doesn't need a household name. They need the most sensible tool for the job, and the CN models right now tick many boxes when it comes to that.

The bubbling froth at the open edge is getting user adopted at a crazy pace, by the early adopter persona trying them all within hours to days. This persona loves taking apart and putting together novel things, and telling others.

Fast follower persona clusters around emerging zeitgeist across the tellings. At the moment, arguably that's mostly Qwen for everyday hobbyists, and GLM for those that can run 512GB to 1.5TB of memory. This persona is seeking viable applied results: "I have frontier at home".

The early majority pick things up after models are curated into apps like LM Studio or one's platform app of choice, usually at least one major release behind because it takes that long to choose and package into mass distribution.

This is the step where early majority persona "has no idea" what the parade of weird names is about, they care about qualia of the conversations they try to have.

This persona is, at present, very under-served, and likely to remain so until mass devices can perform feeling like 27B at Q4 large quality better, or workplace devices can achieve a pragmatic utility like 135B at Q8 or better.

Harnesses that work where the workplace persona lives bridge this. This persona doesn't care the Chinese model name, they care "does it code?" For that, the applied harness and model take time to be matched, as JetBrains did harnessing a tailored Qwen 3.6 in the IDE. More efforts like https://www.jetbrains.com/junie/ are needed for the majority persona to perceive value from changing their workflow again.

HN's "job" is better outcomes with less friction at each persona.

There's no brand confusion, you are just unfamiliar with them.

> these models have no chance at end user penetration and loyalty until there's a single focused survivor.

This reminds me a lot of media horse-race reporting, saying that "candidate X has no chance unless they" and "candidate Y has a strong showing in", and it's very thinly cover for the publication liking Y and disliking X, avoiding talking about actual policy, and trying as much as they can to make their predictions self-fulfilling.

I have seen studies from MIT and Stanford that the majority or US startups are using much less expensive open weight models so consumers of their products are open model users whether they know it or not. These are often Chinese models.

Not to go off topic but I am pleased to see open model support from US companies like Poolside.ai, NVIDIA, IBM, Google, etc.

Consumers aren’t the customer.

Just because you're confused doesn't mean that there is general confusion here. Its really not that complicated.

There will never be any significant loyalty in this field, models are commodities, we will keep switching to the best/most convenient for a long time.

Proper usage in applications will go through similar considerations.

The only "captive" users will be non-tech enterprise users, but they are already in Gemini/Copilot land because they are natural extension to existing Google Cloud/Teams plans and nobody cares about what models are inside those, procurement, contracts and data retention are what matters.

The price is free for some of us, we can run it at home.

You used it for visual tasks, right?

It doesn’t rival deepseek v4 flash, and of course not deepseek v4 pro. This is my own impression.