GLM-5.3-Flash Intelligence, Performance and Price Analysis
artificialanalysis.ai137 points by theanonymousone 18 hours ago
137 points by theanonymousone 18 hours ago
Several factual errors about the model here. The input modalities are listed as text only, but the headline feature is image support. The context length should be 1048576 (so should GLM-5.3's, also wrong on the charts).
Looking at these numbers IMHO, with Gemini you get the speed what you pay for.
Intel Cost
lig per
ence Task Speed
Gemini 3.7 flash 56 0.40 338
GLM 5.3 flash 57 0.09 49
Factor 1 4.4 6.9
I find the Time per Task[0] metric more helpful, because models vary enormously in the tokens required to complete a task. On Time per Task, Gemini 3.7 Flash is Matched with GPT-5.6-Sol, as well as on price per task.
GLM-5.3-Flash takes 7x (relative to Gemini and Sol) per task. So, it's cheaper, if you don't value your time! Don't value real-time workflows, don't value iteration speed, etc. So, doesn't seem very suitable for interactive or agentic work to me.
But having an ultra cheap model for async stuff is always very nice. (Still, the last few weeks feel less about tech and more like a contest between who can afford to give the biggest discounts!)
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I also like DeepSwe[2], although they measure Output Tokens and Agent Steps, which are misleading when one model has a much faster output speed. (e.g. on their metrics Gemini looks slower, because they don't account for that.)
[0] Time per Task - https://artificialanalysis.ai/?models=glm-5-3-flash%2Cgemini...
[1] Output Tokens Per Task - https://artificialanalysis.ai/?models=glm-5-3-flash%2Cgemini...
The analysis is still not compelling for me to switch from gtp5.6-luna to GLM-5.3-flash given
- costs per task $0.05 vs $0.09
- speed 130 vs 88
- where GLM has only 5 more intelligence point: at this point few point is meaningless for most of models
https://artificialanalysis.ai/models/comparisons/glm-5-3-fla...
Been using Luna exclusively since the price drop, and i've been very satified with all tasks from planning, writing code, and other agent tasks. (just change thinking level from low <-> ultra)
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btw, I did try out Ox Alpha, the coding feels good but still not way better for me to switch to it.
Luna is at a very compelling point on the price/performance curve.
I have found that sometimes a smaller model with max reasoning is actually more expensive than using the next tier model with a lower reasoning effort. It’s certainly faster.
Agreed. with "Ultra" (higher than Max), the luna performs really well for my non-metric-backed personal experience
Which has a better monthly plan? Right now Z.ai "Pro" plan (the middle one) is $56/mo if you prepay for a year.
I signed up for their Lite plan when it was only $28 for the whole year (less than $3/mo). Definitely very happy with that purchase!
I’ve been super pro-Luna lately. I really hope that Gemini-Flash-Lite is positioned to compete with it. We all know that Anthropic has abandoned Haiku and it would never be that cheap.
Probably shouldn’t say this here but I’ve been planning to up my $20/mo exploratory ChatGPT subscription to the $100/mo tier as soon as I hit my cap. Between the progress and quality of Luna and their continuous resets, it’s been a few months now that I’ve lived off the $20 tier, frankly waiting for the need to upgrade, credit card in hand.
I’m always trying new models, like many of us here, but the price is just so good for a well balanced, American, hosted model.
Funny, I used to use Gemini before Luna as it was "good enough" and cheap.
For me at this point, most of newer models are capable enough, I focus more on $ and how much I can save.
> The analysis is still not compelling for me to switch from gtp5.6-luna to GLM-5.3-flash given ...
So Luna is competitive because a few weeks ago they did a 80% price drop?
Many here said that 80% drop was not a move against Anthropic but a move against chinese models and your comments indicate that's the case.
I don't understand what would possibly make someone prefer speed over output? You'd rather get wrong bad answers that don't work as well very fast?
In general I really don't mind waiting 5, 10, 40 minutes. There's other things I can look at, other plans or assessments or outputs aplenty stacking up. Its baffling beyond words to me that anyone would take speed over good output. Surely the better output is going to save enormous time in the long run, have better outcomes. What is it that addicts people so much to speed, especially when the difference is between fast and very fast?
Background agent services that require no super human vision.
You might think faster is better when you vibe code or monitoring. But with background agents, the faster the speed, the more jobs it can perform.
The speed won't matter as much for your personal projects, but if you want to handle enterprise level request, the faster the better.
Say you queued up all messages for a task in say Kafka, you got workers calling AI agents. You will have thousands of messages to do, and the faster the AI agents can do its work the better you can clear the queue.
So how exactly is Anthropic and OpenAI ever going to pay back the trillions that they plan on spending?
> So how exactly is Anthropic and OpenAI ever going to pay back the trillions that they plan on spending?
It's really simple: if they truly get to human-level AI (or even superhuman AI), then money and debts no longer matter, since our current economic system will be obsolete. They are betting everything on this outcome.
I don't know if they will manage to do it before their debts have to be repaid, but considering the rate of acceleration in the past few months, there is a non-trivial chance that they will, IMHO. We will see.
That’s like building F1 cars and thinking you will soon have a rocket to land on the moon.
LLM has nothing to do with AGI.
I would perhaps agree with you just a year ago. But now I am not so sure. It is clear that scaling up Transformers still leads to significant improvements, and they are now solving math conjectures and finding real vulnerabilites in software. We don't really know where the capability ceiling of the current approach is, and anyone telling you that we know it is lying to you.
> We don't really know where the capability ceiling of the current approach is
We do know, that the ceiling is below AGI. And it's not a matter of opinion - LLMs can not achieve AGI due to their design. Anyone telling you otherwise is lying to you.
And it doesn't matter how many or how severe bugs they can find, because it's not about what they produce, but how they produce it.
> We do know, that the ceiling is below AGI. And it's not a matter of opinion - LLMs can not achieve AGI due to their design.
[citation definitely needed]
> And it doesn't matter how many or how severe bugs they can find, because it's not about what they produce, but how they produce it.
AGI is defined by the practical outcomes, not by the way the outcomes are achieved. You have no way to know that scaled-up Transformers predicting the next token will never result in human-level intelligence, since we currently have no idea where the ceiling of that approach is.
> [citation definitely needed]
If you're not even familiar with how LLMs work, perhaps you should restrain yourself from confidently talking about this topic until you educate yourself. You're only spreading misinformation.
> AGI is defined by the practical outcomes, not by the way the outcomes are achieved
That for sure would be a very convenient definition, especially for all those AI labs trying to convince investors that they achieved AGI. Unfortunately everyone knows, that knowing the right answer isn't the same as knowing where that answer came from.
> If you're not even familiar with how LLMs work, perhaps you should restrain yourself from confidently talking about this topic until you educate yourself. You're only spreading misinformation.
I am very familiar with how LLMs work, and I am telling you that there is no consensus that they cannot achieve AGI in the machine learning community. Some people think so (such as Yann LeCun), others disagree. We just don't know yet.
> That for sure would be a very convenient definition, especially for all those AI labs trying to convince investors that they achieved AGI. Unfortunately everyone knows, that knowing the right answer isn't the same as knowing where that answer came from.
AGI is defined by capabilities, not methods.
That's assuming that human-level AI is a possibility with current approaches.
I'd assume we want at least human-level intelligence, but better than human-level _decision making_. ;)
You could ask the same about how z.ai, moonshot ai, minimax, and alibaba are going to continue training and releasing models for free.
1. they still have revenue though. it might not enough to cover all the r&d but it is surely enough to cover the hardware cost.
2. people tend to ignore this, but the salary budget of a US frontier lab and chinese frontier lab is nowhere comparable, the first can easily outdone the later by 100x.
3. us labs, like other US style startups, always throw ton of money to capture the market. I don't see the chinese company doing the same scheme at all.
so, surely chinese AI providers also lost money making new models, but they are not spending nearly as much as US ones.
>1. they still have revenue though. it might not enough to cover all the r&d but it is surely enough to cover the hardware cost.
>2. people tend to ignore this, but the salary budget of a US frontier lab and chinese frontier lab is nowhere comparable, the first can easily outdone the later by 100x.
Both arguments make it seem like there's a double standard for american vs chinese AI companies, where american labs are held up to strict standards for profitability, but chinese labs get a pass because [insert handwaving about how some aspect of chinese labs is different]. Let's do apples to apples comparisons here, what are both sides' run rates and revenue growth prospects?
>3. us labs, like other US style startups, always throw ton of money to capture the market. I don't see the chinese company doing the same scheme at all.
Right, instead they're releasing their models for free so competitors can undercut them on inference. American labs' prospect of "there are open models 90% as good but cost less" might seem bad, but chinese labs' prospect of "there are companies offering the exact same models but aren't on the hook for r&d spend" seems even worse.
Literally nothing is known about how these companies are financed. The usual story is that Moonshot was chosen when 'Mythos' occasioned a huge state crisis. China is the ascended masters of insane amounts of capital poured into whatever the state takes into its head next.
By charging $$$ like they do now and having a non terminal business model.
PRC AI have lower opex and capex, i.e. export controls means they couldn't be trillions in the hole on inflated hardware in the first place. They only need to extract a few 10s of billions from domestic market have a healthy runway. If investors/gov wants to throw in a few billion to treat as utility, whatever, it's still rounding error.
Dirt cheap Chinese solar is a competitive advantage just sitting there waiting, but instead the US is trying to revive coal, restart a grossly ineffective small scale nuclear system with immensely bad fuel utilization, and spending billions to cancel renewable projects that were already approved. I'm so tired of these insurrectionist dog traitors to this country I love.
>Dirt cheap Chinese solar is a competitive advantage just sitting there waiting, but instead the US is trying to revive coal,
This seems like a double standard given that china is still building coal.
just like there were mistrals, coheres, llamas, etc, there will be new deepseeks and moonshots if those ever flame out (worst case, given out at cost by google, meta, alibaba or etc)
OpenAI and Anthropic are already in a ~200bil hole from previous model iterations and are committing to trillions of additional spending
OpenAI spent more TBPN than kimi spent on training K3
They are owned by the state, so the economics are a bit different.
>They are owned by the state
They are by all accounts, not. Z.ai for instance is a public company according to wikipedia. Moonshot AI is private but all their investors are private companies. Alibaba, as we all know, is a massive publicly traded tech conglomerate.
Moreover even if we take the more charitable view that they're controlled by the CCP, and therefore will continue releasing models for free, that seems as questionable as the prospect that private investors will continue shoveling money into anthropic/openai.
China has "classroom game capitalism", where companies can play the game, but the teacher still has uncontested, absolute, unilateral control in everything and anything. All the parameters of the game are managed by the teacher, and the teacher is the one who creates the foundations for the direction they want the game to move in.
Don't forget, Jack Ma of "publicly owned" Alibaba, had to go into classroom time out after seemingly forgetting that its classroom capitalism and not real world capitalism.
Let’s not pretend that what is currently in the US is real world capitalism, whatever that means.
The core employees of z.ai are billionaires, because of the equity given to them in the past, which in their system is not antecedently evaluated. Much of the past annual expenditure of OpenAI has been the same, handing out equity - but because of the different legal system it is given an evaluation and listed as expenditure. Meanwhile the expenditure on compute for training and inference are apples and oranges again as the state is all over this with moonshot and z.ai and so on .
By charging $$$ like they do now and having a non terminal business model. PRC AI have lower opex and capex, i.e. export controls means they couldn't be trillions in the hole on inflated hardware in the first place. They only need to extract a few 10s of billions from domestic market have a healthy runway. If investors/gov wants to throw in a few billion to treat as utility, whatever, it's still rounding error.
At a fraction of the cost.
Shoveling 70% less money into a money pit is still shoveling money into a money pit. Not to mention that at least openai/anthropic has better prospects of making back the money because their models are proprietary, and won't be cannibalized by other companies serving the exact same models.
Neither of the two is designed or cares to ever be profitable or make any money back.
Those are Musk-like businesses, on steroids.
Not even Tesla has been profitable compared to the capital raised and the debt issued.
I expect they're going to fight each other to become the vendor of record for the government, and whoever wins will get bailed out. This is one area where they don't have to worry about competition from Chinese models.
Its more Google Amazon Meta Microsoft who are spending trillions. They will be fine. So will Anthropic and OpenAI. Nvidia will presumably survive. The losses are all the real estate interests and contractors and contributory hardware companies etc.
Why do you think tech oligarchs have been cozying up to the Trump admin? They're angling for a government bailout, paid for by your tax money!
Thy cozy up to whoever is in government. They cozy'd up with Biden too.
That's true. But it's been a while since an admin was as brazenly corrupt and the cozying up was so promising.
I think most of us can see that the level of corruption we are seeing with the administration is quite historical. You can’t simply say “both administrations engaged in corruption” and consider the matter closed. Scale matters.
Impressive. It kicked everything between itself and Sol xhigh out of the Pareto frontier. Can't wait to try it out.
Better than the latest Deepseek v4 Pro while being 3x cheaper in cost per task. Impressive!
Can’t wait to try this out, and the only missing from this model for me is Image input support
Why does the top card say "Intelligence #1/173" when the bar chart further down shows it only at position 7?
And the model isn't even shown in the speed bar chart just below. Such slop (the artificial intelligence website linked)
Not clear to me how it selects that, but it considers it to be #1 out of 173 models "of the same class".
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