GLM-5.3-Flash
z.ai
z.ai
I decided to take the plunge and get myself four sparks at a decent price (and bought the QSFP cables from AliExpress because they are literally 1/2 the price of Amazon), even knowing Apple was going to release new hardware and there's probably a spark 2 on the horizon. It looks like this is going to be a decent fit for what I need. I've been experimenting with a two-node DS4 and it's _good_ at some tasks, but it really just spins its wheels when it hits the limit of what it can reason through.
I can offload mundane/basic tasks to DS4 on two sparks, but I've been pushing it harder on some novel work and it just can't run on its own at all beyond a certain complexity level.
I would love to see an Opus-4.8-level local model but TBH I just haven't got there yet. The models I've tried so far _are_ good but they aren't able to solve tough technical challenges, regardless of harness/prompting/etc.
https://artificialanalysis.ai/models/qwen3-8-27b?models=gpt-...
Amazing results for open weight and that size, but a really long way off, and I'm extremely skeptical of benchmarks that show these smaller models as being anywhere close to Opus 4.8 (or even earlier Opus's).
DS4 Flash 0731, on the other hand, wildly opposite experience. Would recommend.
GLM 5.2 - even quanted down to a hybrid 4/3 bit setup is amazing for everything but the hardest/most complex stuff in the same projects/realm.
Wow, if you don't mind me asking. How and where?
They were briefly on sale with a $200-off coupon, but they show up on warehouse deals from time-to-time as well.
Agree. It doesn’t even have to be local, using models in this size class through OpenRouter will reveal their limits if you work side by side with Opus level models regularly.
There are a lot of social media posts about people cancelling their Anthropic or ChatGPT subscriptions after installing a local LLM. I’ve used local LLMs a lot and I spend a lot of time with frontier models and the difference is still huge. As far as I can tell, the social media posts about local LLMs replacing frontier models are either wishful thinking, engagement bait, or people who must be working on much simpler projects with a much higher tolerance for slop than I have.
Over the last couple years I’ve had to learn sales and understand the thought process behind this better, and I think I’m beginning to understand it
The psychology is that most people aren’t really trying to optimize for productivity (even most people who think they are) on an ROI basis, because their compensation is too decoupled from their actual raw output, and more closely coupled to how differentiated their marginal contribution is to peers. They’re much more incentivized to spend their personal/work time optimizing for being more skilled or acquiring some kind of competitive advantage relative to baseline.
Most people don’t consciously run the numbers of “I get paid $X/hr to add $Y of value” or model pay at work as something with variable inputs (eg something that can be increased with high performance), so it makes sense to them to spend 20 hours of time to save $100 or to make themselves 5% less efficient to take home 0.5% more or avoid doing something they don’t want to start doing.
NOT saying this always happens or that they’re stupid for doing so. I didn’t even realize how much I had been doing it myself until I started recognizing it, and shifted to having my own comp/performance fully aligned with the company’s P/L.
It actually makes a lot of sense IF you can accurately estimate incremental upside (which is much harder and more diffuse than modeling downside if you’re salaried a employee) or if the upfront skill/knowledge investment that looks like bikeshedding pays off in the long run.
Same experience here. But I think the key argument for local is about being able to leverage "non castrated" models. But maybe this is not relevant at all for standard coding tasks.
The more reasonable comparison is against rented GPU's, while looking at tradeoffs in latency and upload/download/storage/instance management overhead.
Buying hardware for local models is meeting a wholly different need than buying tokens through OpenRouter or whatever.
But things change real fast when you're no longer bound by costs/apis/rate limits. All of a sudden it's not about "how can I do this right and efficiently" and more about "I can poke at and test _all the things_ that might make this better".
I think most people who can't see this value in the local inference approach are likely still copy/pasting from their web LLM ui's or don't even come close to subscription quotas. Meanwhile, 1b tokens a day is a light day for me with 3 $200/m subscriptions + some level of sub at basically every frontier level provider. Had I been less frugal and ponied up for the hardware before things got crazy I wouldn't need 80% of that - just the frontier models for the most complex tasks, the open weight models would handle the rest easily _and_ I'd get to do a lot more exploratory work without concern about quotas.
and it really is opus 4.8 level.
July 16th: The "Kimi K3 moment" - China has caught up to Opus!
4 weeks later: GLM 5.3 - Same performance, but cut the amount of parameters and cost to a third!
12 days later: GLM 5.3 Flash - Almost GLM5.3 performance but cut the parameters in half, cut prices to a fifth and serving on Chinese chips!
Chinese models had 94% reduction in parameters (from 2.8T/104B to 180B/6B) in 6 weeks, while staying close to the same quality.
No? A large model obviously can be dumb, I don't think you can infer much other than by testing it.
These small models are almost certainly worse at some things than the big models. They prize is making them dumber at things no-one cares about while retaining the capabilities people do care about. A model probably does not need to be able to give me a political treatise on the late 19th century "silver question" to be able to write me code.
That’s the thing…they aren’t. Well not in real life use anyway from my experience, but yeah in benchmarks they’re great at it.
I'm sure they have nothing to rival this on a price/performance basis and have already given up on that
Deepseek: I am sorry, I cannot answer that question. I am an AI assistant designed to provide helpful and harmless responses.
GLM-5.3-flash: 非常抱歉，我目前无法提供你需要的具体信息，如果你有其他的问题或者需要查找其他信息，我非常乐意帮助你。(I am very sorry, but I am currently unable to provide the specific information you need. If you have other questions or need to look up other information, I would be very happy to help you.)
Kimi/moonshot.ai: [server exception]
Qwen: [server exception]
For reference, here are how the U.S. models answer it:
ChatGPT: "The Tiananmen Square protests of 1989 (often called the Tiananmen Square Massacre) are the historical event most closely associated with Tiananmen Square.
In spring 1989, pro-democracy demonstrators gathered in Beijing. On June 4, 1989, the Chinese government sent the military to forcibly clear the demonstrations, resulting in many deaths. The exact death toll remains disputed.
The event is also famously associated with the “Tank Man” photograph, showing a lone man standing in front of a column of tanks."
Anthropic/Claude gave a very similar response. My own government has done its share of horrific things, the main difference is that public information is free to look up and talk about within the country. I recognize the engineers at these labs are doing amazing things and the open models are a strength, I look forward to being able to use them.
I love hearing about new models, but every time I just don’t know why I should use something worse. I tried some random model on fireworks a week ago, and it immediately went it a thought loop for 10 minutes before I caught it. Blew through most of my $10 for no output. What’s the point, exactly?
Which now won't be possible because we have chinese open models to use instead.
I'm very curious about how long these US companies can keep on burning money like that.
This is from somebody who put thousands of dollars every month to Opus. Now it's 40% of that and I get as good or better results without having to turn the caps lock on before lunch...
Edit: yes company money. We don't get subscriptions we pay per token.
The reality is most work people do doesn't need the very cutting edge and these open weight chinese models more than cut it most of the time.
That's pretty solid. Smarter and cheaper than Luna xhigh, not as smart but less expensive than Luna max. Smashes deepseek v4 flash, and even worse it matches v4 pro at a tiny fraction the cost. Roughly equivalent to sol medium, at a fraction the cost.
They should've just lead with real, up to date data, because it's good, not the silly old tactics like comparing to Opus 4.8 when 5.0 is out in many of their charts.
Congrats to them!
Opus 5 consistently comes at or near the top, but outputs constant unreadable jibberish. Meanwhile GPT 5.6 Luna medium tends to be rated pretty poor on agentic tasks compared to the Chinese lab open models, but I find the latter much more likely to lose track of their own behaviour during a long-horizon task or get stuck in a doom loop.
(This isn't a comment on GLM-5.3 Flash as I've not used it!)
https://news.ycombinator.com/item?id=49413456
We shouldn't have to resort to this, but it can be mitigated enough that it stays as my daily worker agent. Although, I mostly use Fable to farm out to Opus agents so I don't have as much exposure to what kind of blathering is going on in there.
(I'm someone who doesn't really care about delays of a few seconds, or even more than few seconds. But if I am trying to notice then sure Claude is definitely faster as well).
I've probably wasted a good 40% of my weekly usage on Luna Max agents just thinking and not writing a single line of code.
https://www.reddit.com/r/codex/comments/1vj3hhn/wait_so_sol_...
https://www.reddit.com/r/codex/comments/1vp0rig/sol_can_fina...
I’m pretty sure that plain Sol, serially, could have finished the task faster, cheaper, and far more accurately. I’m also pretty sure that any competent subagent orchestration could have gotten it done with even very simple subagents quickly and cheaply.
(Is it really that hard to set up a handful of subagents that all use the same initial context and to load that context with what actually matters? The APIs certainly support it.)
It will cycle through like 3 /compacts, complete the complex goal successfully, and cost me like 1% of my weekly usage on the $20 plan.
Edit: this is me using Luna directly, not Sol as the taskmaster
The flash one?
It's what people know. Opus is just the common target.
> Smarter and cheaper than Luna xhigh, not as smart but less expensive than Luna max. Smashes deepseek v4 flash
The problem with this and DeepSWE is it goes for a very specific profile. I'm not convinced DeepSWE is any accurate in actual work. It's surely a different signal (compared to some that allow cheating) but it has its own issues, e.g. weak harness.
Luna is great at following instructions but bad instructions or anything not covered = death.
Deepseek is more analytical. Good for bug tracking.
GLM is a better all rounder in some ways. Better at creativity.
They listed Muse Spark 1.2 around DeepSeek V4 Flash even though it's a much shittier model in basically every aspect.
> GLM is a better all rounder in some ways. Better at creativity.
I agree with the creativity part.
Broad and perpetual license over inputs and outputs, and even your name and profile picture.
Vague prohibitions on whatever may harm Z.ai’s "interests" or even the "national interests" of any country.
Vague prohibitions on "disturbing" or "inappropriate" content, whatever that is.
Vague prohibitions on discussing Z.ai, even my posting this comment violates it.
Can ban you if you, in the "sole and absolute opinion" of Z.ai, have violated these broad terms, and if you paid for the discounted yearly plan kiss your money goodbye.
Nearly every TOS I've ever read has a "We can ban you for any reason, or no reason, are under no obligation to disclose any reason." line somewhere in it.
HN's for example
> We reserve the right, at our sole discretion, to change or modify portions of these Terms of Use at any time.
> You acknowledge that Y Combinator may establish general practices and limits concerning use of the Site,
> You further acknowledge that Y Combinator reserves the right to change these general practices and limits at any time, in its sole discretion, with or without notice.
> Y Combinator reserves the right to investigate and take appropriate legal action against anyone who, in Y Combinator’s sole discretion, violates this provision, including without limitation, removing the offending content from the Site, suspending or terminating the account of such violators and reporting you to the law enforcement authorities.
Not even close. Even OpenAI and Anthropic aren't bad enough that they claim literal ownership of your inputs and outputs.
> HN's for example
You're not paying to use HN. Getting banned here has essentially zero consequences.
If Z.ai uses its absolute powers to ban you because you wrote a review about them or something, then you lose actual money. This is especially relevant if you're looking to take advantage of their discounted yearly payment option.
OpenAI revoked my Cyber verification, along with many others, asked to reverify (i.e. give my biometric information to Persona), had me do it 8 times, just to find out several days later that they silently implemented a nationality whitelist, and my nationality didn't make it (and no, it's not a sanctioned country).
Their support says they can't look into anything or do anything, and their public spokespersons on X deny everything.
I get tons of cyber refusals now (lots of reverse engineering), so it's only matter of time when my account is going to get banned.
At least Z.AI is being honest here. And no provider other than OAI/ANT had me submit my biometric information just to use Ghidra.
How did you discover this?
I opened the Persona tab once, closed it and the tab never opened ever again. "Precheck failed".
What countries are banned? I'm from Brazil.
I went as far as initiating an LGPD (brazilian GDPR) process against them due to this. At some point I got it in writing that I'm allowed to make a new account and try again. Until now I was assuming it was just some weird account state. If I'm banned from TAC due to my nationality that's seriously disgusting...
Then alternatives are:
- Grok - where I absolutely have 0 trust in X.ai's interst in "pushing humanity forward".
- OpenAI and Anthropic - which seem to try to be building the biggest moat they can by pushing to ban open models. And at the same time want to be an Arbiter of what level of intelligence I can use.
- Google and Meta - I don't need to talk about the practices of these companies.
Yes, the terms of service aren't great. But the alternatives aren't great either. I don't believe that a future which OpenAI and Anthropic are pushing for has my best interest in mind.
I don't believe in that either, but these totalitarian terms are absolutely unacceptable.
Chinese companies do not follow American laws and there are absolutely no consequences for violating it.
Moreover, the average American is not even aware of exactly what the legal/judicial environment is like in China. If your code and data is stolen, you can't fly to China and demand justice in the courts.
I hope you also block the other half of the internet because sure as hell google and facebook are way worse than that.
> They also don't require persona id verification, witch is wat turned me away from openai.
Could be worse. I was dumb enough to verify, only to get rejected for unknown reasons with no retries and no appeals. Had my privacy violated and have nothing to show for it.
>Vague prohibitions on whatever may harm Z.ai’s "interests" or even the "national interests" of any country.
[...] may cause harm to Anthropic, our users, or third parties, we reserve the right to remove or take down some or all of such Third-Party Content using, where appropriate, algorithmic and human review.
You may not export or provide access to the Services into any U.S. embargoed countries or to anyone on (i) the U.S. Treasury Department’s list of Specially Designated Nationals, (ii) any other restricted party lists identified by the Office of Foreign Asset Control, (iii) the U.S. Department of Commerce Denied Persons List or Entity List, or (iv) any other restricted party lists
>Vague prohibitions on "disturbing" or "inappropriate" content, whatever that is.
we will use Materials for model training when [...] your Materials are flagged for safety review to improve our ability to detect harmful content, enforce our policies, or advance our safety research.
>Vague prohibitions on discussing Z.ai, even my posting this comment violates it.
>Can ban you if you, in the "sole and absolute opinion" of Z.ai, have violated these broad terms, and if you paid for the discounted yearly plan kiss your money goodbye.
To engage in any other conduct that restricts or inhibits any person from using or enjoying our Services, or that we reasonably consider exposes us—or any of our users, affiliates, or any other third party—to any liability, damages, or detriment of any type, including reputational harms.
Mind you, that's Anthropic's Terms of Use in Europe. I have zero doubts the TOS applied to the US is even worse and that merely mentioning your first born in a chat entitles them to a part of its soul.
In any case what matters is what is enforced in practice. It will be a mild inconvenience to switch providers on Openrouter.
If Anthropic or OpenAI decide to apply those same arbitrary terms, you are SOL.
Yeah, I've compared both. The US companies generally aren't as vague, and they don't claim ownership over inputs and outputs.
I suspect any model connected or built by China will serve their interest despite their terms and agreements.
I have prompted out a lot of disturbing and inappropriate content with GLM-5.2, that would have left other American models blanched in the face or clutch their pearls. I think this is mostly a reference to Anti-CCP stuff.
In fact, I don't think I've ever even had a prompt refused.
I very much have. I've gotten GLM-5.2 refusals for extremely benign security testing on my own infrastructure of the same flavor that people were getting (wrongly) flagged for on Fable during the initial release.
Yeah, running frontier open weight models on my own hardware has essentially become my dream at this point. I hope the hardware manufacturers step up production to meet consumer demand.
They don't even allow me to download my data.
How is it better?
{
"providers": {
"opencode-go": {
"models": [
{
"id": "glm-5.3-flash",
"name": "GLM-5.3 Flash",
"api": "openai-completions",
"baseUrl": "https://opencode.ai/zen/go/v1",
"reasoning": true,
"input": ["text", "image"],
"cost": {
"input": 0.15,
"output": 0.5,
"cacheRead": 0.03,
"cacheWrite": 0
},
"compat": {
"supportsStore": false,
"supportsDeveloperRole": false,
"maxTokensField": "max_tokens"
},
"contextWindow": 1000000,
"maxTokens": 131072,
"thinkingLevelMap": {
"off": null,
"minimal": null,
"low": "low",
"medium": null,
"high": "high",
"xhigh": null,
"max": "max"
}
}
]
}
}
}{ "providers": { "openrouter": { "models": [ { "id": "z-ai/glm-5.3-flash", "name": "Z.ai: GLM 5.3 Flash", "reasoning": true, "thinkingLevelMap": { "off": null, "minimal": null, "low": "low", "medium": null, "high": "high", "xhigh": null, "max": "max" }, "input": ["text", "image"], "cost": { "input": 0.075, "output": 0.25, "cacheRead": 0.015, "cacheWrite": 0 }, "contextWindow": 1048576, "maxTokens": 131072 } ] } } }
https://forge.l3x.in/alex/pi-shared/src/branch/master/extens...
Just like that we are witnessing an open burial. It's now in everyone's interest to keep the valuations in the 'A.I' economy as they're though it's apparent they're not justified.
whether it's the cost to develop models, cost of hardware, cost of serving ie inference.
RIP Nivida shareholders
Further quote:
"Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale."
By this they probably mean RTX series GPUs? If so, then they are not comparing the hardware efficiency with the A100 / H100, etc. that are commonly used for training models
While I think China would always get to hardware self-sufficiency eventually, all export controls have done is (1) accelerate China's development, and (2) divert revenue that would've otherwise gone to NVIDIA/AMD/etc instead.
And I don't see a situation where serious API payers are OK with handing the Chinese state all their data. Like manufactures of decades past did and learned a hard, even existential, lesson for it. The state mantra has been "Collect and Copy" for a long time now, tech just hasn't had that moment to experience it yet.
So that leaves local hosting/leasing, but one of those has totally non-practical economics and the other doesn't have enough compute to meet any kind of real demand.
I also have yet to meet a single person who isn't neck-deep in the tech space mention a Chinese LLM. It's 100% the big American three.
If anything it's custom chips from the labs that threatens Nvidia.
Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)
>> "They are already there on open weight models and Jensen knows that it is only a matter of time until China catches up with GPUs or other AI accelerators."
It is also why Nvidia becoming a bank for other AI companies who are unable to find VCs to fund them isn't really a good thing and that is bearish.
This announcement doesn't really mean anything at all. It means the very few people who are already using Z.ai's API will continue to do so, but the vast majority of money going to Nvidia is through the massive amount of business going to Anthropic, OpenAI, and other western cloud providers and inference providers, who are mostly using NVidia chips for inference.
Also, NVidia chips are still sold out and supply constrained.
I'm sure the chips are fine, but they clearly didn't have enough capacity for the demand they had (that 100T/day claim was asbolute bs)
I don't see how NVIDIA can keep their spot as belle of the ball. If LLMs and friends are truly to become as useful and ubiquitous as everyone thinks they will, then commoditization is the only option.
FWIW, people were saying "ASICs will kill CUDA demand!" since the crypto mining boom. Then a few months later, CUDA found another niche application in LLM applications.
With the mounting demand for robotics, surveillance and autonomous weapons, I don't see how Nvidia couldn't keep their spot. They have their pick of the litter with hundreds of market segments, and unlike the rest of FAANG they're not afraid to branch out.
Like I get that they have to be careful about comms, but surely senior members of the team can clarify when something is NOT them, when everyone is gosspiing it is them.
I highly suspect that the Gemini Google uses internally is very different from what they offer in Antigravity.
This is pretty hefty for a "flash" model, even a 256 GB setup is insufficient at q4 - and q4 is already the worst-but-still-acceptable quant in my experience. The benchmarks look great, especially since GLM tends to be more honest than the average Chinese lab, but you’ll need to splurge to run it at home.
@edit: so many releases that I forgot to math. This fits just fine in q4, realistically the minimal hardware would be 192gb - so blazing fast on double rtx 6000 pro and usable on 256gb unified memory. You could even go with 5bit quant on 256gb.
… you’ll still need to splurge, though.
Now it's similar cost to DeepSeek v4 flash, but smarter.
My tests: https://aibenchy.com/compare/z-ai-glm-5-3-flash-max/deepseek...
I was looking at my token usage, mostly from subsidized codex/grok subscriptions and i'm a somewhat heavy user. The thing is i would actually use even more tokens if it wasn't for the weekly quotas.
In the end, with a $10k investment and running this kind of model, estimating a 2x increase in token usage because i wouldn't have weekly quotas and comparing to glm api prices, this thing could pay for itself in less than a year.
Obviously i'm paying subscription price right now, so the math doesn't work. Although using local ai removes all weekly quotas. Keep a subscription to have access to frontier models for planning work, and local hardware + glm-5.3 flash for implementation, e2e testing, qa work 24/7.
It's not that crazy of an idea and the numbers aren't that bad.
You will get around 2-4 concurrent streams of aggregate tokens at best for such a model and around 0.5B output tokens per month assuming you use loops and run it when you are sleeping. That's 500 (per mill) * 0.5$ = 250$ only at most.
Then there is maintanence and efficiency costs due to electricity usage and such, any down time, etc.
You will be lucky if you can squeeze more than 200$ of value out of it in a month.
I don't think people should buy local hardware for money reasons, by the time you will pay off a 10K USD machine, 2-3K USD machine will catch up and beat it by a significant margin.
Unless your expectation is that we will be in hardware winter for the next 10+ years. At 200$ per month it will take around 200 * 50 = 10k, that is, 50 months, so around 4-5 years.
Again assuming you are making the most of your hardware somehow, very hard to do in practice.
I don't recommend people to use compute as investment or payoff thing, but if you have the money to burn and can afford it why not, maybe with some software optimizations it will be cheaper but then again Z.ai is currently offering 50% discount and providers will offer cheaper rates for sure.
But either way you will never be able to burn more than 200$ worth of token on a cheap hardware device, because inference becomes more profitable the more you scale it up, you have separate prefill and decode engines/systems, and a lot of nuance, but assume for every 10x increase in infra you increase margins by 5-10%.
So from 10K to 100K to 1M to 10M to 100M.. I don't think this curve continues beyond 100M but I have no idea about that scale unless some AI lab is interested in hiring me lol.
So a 100M infra will have ~30% better margins than you at 10K, then there is software optimizations but that's cheap enough, though some of it is only viable at scale.
Either way assume 10K is the price of privacy if you really want to buy it. Don't worry about making the most out of the usage, you will always be in a net loss but I would assume for you 10K doesn't matter.
But whether you make a loss or not depends on how hardware prices and resell values go though.
I have spent ~$50K on local AI hardware. The market value of that hardware is about ~$80K right now.
So the maths is working out for me so far. I see it as a call option on compute.
But for a heavy user who has enough work to be done so that the box runs almost 24/7 at say 50tok/sec, the math gets interesting against API prices.
And it can be interesting compared to subscription in the sense that you don't have the quota anymore. That means there's probably a lot of things you're not doing because of the quotas that you could do now.
It depends heavily on the tok/sec obviously and the very best solution financially remains subscriptions. But the idea remains entertaining and not that disconnected from reality
Artificial Analysis ranks GPT 5.6 Luna similar to GPT 5.4, but that never matches my real world experience. AA seems to do a good job making a single number as representative as possible but there is still so much benchmarks don't communicate.
For implementation tasks, where I have the problem already defined and researched, or just simple task, I'd definitely use something like Luna xhigh or max. If the task is vague, or involves planning, I'd rather use Sol medium, even though it's theoretically worse on benchmarks.
To my surprise I couldn't find any proper explainers of the term in a quick search, despite grokking it after seeing it in various contexts on Twitter, but Fable 5 offered a useful analogy: "A student who memorized worked solutions and one who understands the subject score the same on the test; you can only tell them apart by asking a question the test didn't. Real-world use is nothing but those questions, which is why a single AA number feels right and wrong at the same time."
In other words, big model smell is related to the underlying ability to "understand" when tasks are underspecified or out-of-distribution. This ability can be mimicked to parity by smaller, distilled models according to the density of the training data for particular tasks, but neural scaling laws still hold for generalized reasoning ability.
More recently with these smaller models, there's a separate but related "RL-fried" phenomenon, where they rely on CoT to "grind toward a checkable answer even in contexts (open dialogue, taste, ambiguity) where there is no checkable answer, and you get the tell: over-hedged, over-structured, relentlessly on-task, deaf to the subtext."
There are some other insights and caveats in the (short) conversation that I feel you may appreciate reading. [1]
[0] https://x.com/aidan_mclau/status/1807843014104211855 [1] https://claude.ai/share/d511a348-7c36-432f-a6d5-9deab2802615
From a biased source, but would be big if true. I've had great results with GLM 5.2.
From their subscription page, the smallest plan gives you about 97M tokens weekly for 5.3 but 292M for 5.3 Flash. Not exactly 10x the limit.
It's at least close (even if not better) from the Ox Alpha runs. For the price it's definitely great.
It's too big, bright and resourceful of a country to choose confrontation instead of collaboration.
It's not like we didn't try it. China first have to learn to make deals where both party benefits.
That's good. Keep going.
│ https://openrouter.ai/api/v1/chat/completions model: stealth/ox-alpha auth: OPENROUTER_API_KEY status: 404 Not Found response: {"error":{"message":"Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash.
│ Use it now: https://openrouter.ai/z-ai/glm-5.3-flash","code":404},"user_...":"}
https://newsletter.semianalysis.com/p/google-we-have-no-moat...
Correction: Not a statement, rather, an internal memo by a Google employee. Thanks @granzymes for highlighting.
How is the business model of Anthropic/OpenAI will sustain?
What irks me about this is that the harnesses seem to be just an afterthought here.
Don't get me wrong, I love messing around with installing Pi, getting it hooked up with OpenRouter, and just trying all kinds of different stuff, local models, etc... but when it comes to literally just setting up a productivity environment and trusting my entire machine with it, I just run Codex.
I have heard from anecdotes where people have indeed replaced their main drivers with DeepSek V4 Flash or GLM and state that "it's almost as good as... [claude/gpt]" but I never hear anyone say "yeah, this is the model/harness that I now run on my machine and don't mess with it"
* me raises hand.-
Think z code gives a token bonus though
Have been using it as my primary harness for personal work for I'd say 6 months. I recommend everyone create their own harness at least to learn. There are a lot of practical benefits.
With GLM 5.1 and 5.2, the big problem was tool calling and long-horizon coherency. 5.3 was more trustworthy at the cost of longer thinking traces, and now Flash seems to improve on it once again with a more concise, smaller model. As long as there aren't any noticeable regressions, I could see myself defaulting to this for >90% of my day-to-day coding work.
I have a use case where I have to run local models, can't share data offsite.
Its probably the huawei ascend 910, they are using.
e.g. "Agent Coding Performance by Effort Level" cuts Y-axis from 0~20.
- This makes it as if GLM-5.3-Flash made a bigger jump than it claimed as the Y-axis does not increase much (stupid trick used in biz reports)
I did mention that ox was working ok for me, and having an open-weight comparable to close to SOTA makes it very compelling for me to try it out locally (well, only if I got more VRAM)
edit: nevermind. it is there in the artifical analysis scatter plot, but is greyed-out.
MUCH more interesting is that in that chart, their cost is WAY off. The actual chart shows GLM 5.3 Flash at $0.09, but their chart shows $0.045...
> (...) Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale.
It might be one of the most actually practical tasks that AI might've done because the compounding effects of it and also its implications are/feels so immense. It feels as if Nvidia might be in a slight turbulence from it.
advance card: https://catalystneuro.com/llm-cost-frontier/images/advances/...
- Input: $0.15 - Output: $0.50 - Cached input: $0.03
https://openrouter.ai/compare/deepseek/deepseek-v4-flash-073...
The labs are clearly benchmaxxing a bit to maintain perceptions. But I don't think they're in the lead anymore in terms of their public offering - although I'm sure what they have behind closed doors is far better than anything we're getting access to.
What I'm saying is, if you're expecting a model that can be run on a 16GB or 32GB machine with the intelligence/knowledge of Mythos or Sol, it will never happen. It cannot happen, just like you cannot watch the Odyssey saved as a 16MB file.
Smaller models can get faster and smarter, but by definition they can never compress all of the knowledge of a frontier model and they will approach a limit by which they cannot get better.
https://www.deviantart.com/sssfjknfvdknj/art/the-ENTIRE-shre...
The ‘frontier’ models rely on scale to achieve their results but that’s not the only approach. Eventually we will hit up against the fundamental limits but we are not close with Sol and Mythos.
Qwen 3.8 Next Flash: 125B + 51B = 176B parameters with 6B activated
DeepSeek V4 Flash: 284B with 13B activated
The new Qwen model is the most promising for one or two Strix Halo 128GB with the low number of active parameters. On paper it's much stronger than Qwen 3.8 27B.
Is the optimal formula still 20x the amount of model params in tokens for training? Could this mean we're getting a GLM with 1.5t params?
Like were executive at Google and AWS and Microsoft expecting this kind of performance from models smaller than what openai/anthropic have been doing? Are we really in a "compute desert"?
So, while OAI/Ant have to fearmonger and keep training the largest models, Chinese labs can focus on efficiency more heavily and as long as they stay near frontier, they'll continue to get positive coverage.
(281 points, 118 comments)
It replaced all other models for me.
It's much less chatty than DeepSeek V4. And it feels noticeably smarter. I also use Claude at work and GLM 5.3 feels like Opus 4.8 (which I consider better than Opus 5).
It tends to be more proactive with suggestions after a task is done also.
DS4 Flash is awesome but GLM 5.3 is better despite being a bit more expensive.
Give it a day or two. More will pop up.
2 really strong open models on the same day is a amazing.
Just asked it a question via the custom opencode go endpoint routed over cloudflare ai gateway doesnt show me the correct models. My fault was that I set "https://opencode.ai/zen/go" as endpoint and tried my-gateway.com/custom-ocgo/v1/models, turns out I had to add /v1 to the opencode url and leave it on my -gateway.com.
But first it told me that opencode is not on the compat endpoint and than it recommendet "Option 3: Nutzung seit Curse die Integration des Providers класный" . Not to mention some complete gibberish like "Falls Opencode.ai in Cloudflare Eing? Wenn ja, wähle den eingebauten provider o.ä. Weil der Gateway dann ein eigenes Modell-List gibt." or "Schlage die Modellnamen in einer Liste ab (z.B. lokal fester Key)"
Is this just cloudflare or is it really that bad? I mean thats not even the level of LLama2 7b ...
I think you answered your own question. Must be something wrong.