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GLM-5.3-Flash Intelligence, Performance and Price Analysis
- m_ke 2mo agoSo how exactly is Anthropic and OpenAI ever going to pay back the trillions that they plan on spending?
- vrganj 2mo agoWhy 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!
- ptdcc 2mo agoThy cozy up to whoever is in government. They cozy'd up with Biden too.
- vrganj 2mo agoThat's true. But it's been a while since an admin was as brazenly corrupt and the cozying up was so promising.
- Forgeties79 2mo agoI 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.
- Haven880 2mo agoSocialize the lost.They dont have to. You pay.
- gruez 2mo agoYou could ask the same about how z.ai, moonshot ai, minimax, and alibaba are going to continue training and releasing models for free.
- epolanski 2mo agoAt a fraction of the cost.
- gruez 2mo agoShoveling 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.
- epolanski 2mo agoNeither 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.
- m_ke 2mo agojust 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
- WarmWash 2mo agoThey are owned by the state, so the economics are a bit different.
- gruez 2mo ago>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.
- Marha01 2mo ago> 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.
- my002 2mo agoThat's assuming that human-level AI is a possibility with current approaches.
- traverseda 2mo agoNo, it's assuming there's a non-trivial chance that they will.
- thatwasunusual 2mo agoI'd assume we want at least human-level intelligence, but better than human-level _decision making_. ;)
- skeledrew 2mo agoNo need even for "human-level" per se. It just has to be useful enough that it upends the current economic order, which it's already well on the way into doing.
- LeBit 2mo agoThat’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.
- Marha01 2mo agoI 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.
- yogthos 2mo agoI 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.
- applicative 2mo agoIts 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.
- AnodicElegy 2mo agoImpressive. It kicked everything between itself and Sol xhigh out of the Pareto frontier. Can't wait to try it out.
- deleted 2mo ago[deleted]
- a012 2mo agoCan’t wait to try this out, and the only missing from this model for me is Image input support
- minimaxir 2mo agoIt has image/video input support (that is surprisingly good)
- CGamesPlay 2mo agoSeveral 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). https://docs.z.ai/guides/vlm/glm-5.3-flash#model-api https://docs.z.ai/guides/vlm/glm-5.3-flash#model-api
- hn7jmxa7oc 2mo ago[dead]
- jhack 2mo agoBetter than the latest Deepseek v4 Pro while being 3x cheaper in cost per task. Impressive!
- croemer 2mo agoWhy 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)
- yipinwong 2mo agoThe 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-flash-vs-gpt-5-6-luna 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) --- btw, I did try out Ox Alpha, the coding feels good but still not way better for me to switch to it.
- psadri 2mo agoLuna 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.
- yipinwong 2mo agoAgreed. with "Ultra" (higher than Max), the luna performs really well for my non-metric-backed personal experience
- TacticalCoder 2mo ago> 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.
- yipinwong 2mo agoI didn't bat an eye before 80% price drop. I used GLM-5.2 and Gemini flash
- vineyardmike 2mo ago
- deleted 2mo ago[deleted]
- smartbit 2mo agoLooking 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 have both GLM & Gemini in a subscription and see no reason for choosing GLM 5.3 Flash. Working with de speed of Gemini 3.7 Flash is such a delight that I accept the hassle of working with Antigravity CLI, coming from Claude Code which I use for GLM.
- andai 2mo agoI 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!) -- 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-3-7-flash%2Cgpt-5-6-terra%2Cgpt-5-6-sol-medium%2Cgpt-5-6-sol-high%2Cgpt-5-6-luna%2Cdeepseek-v4-flash%2Cgemini-3-7-flash-medium&intelligence-efficiency=output-tokens-per-task&intelligence-comparison=intelligence-vs-time-per-task#intelligence-comparison-tabs https://artificialanalysis.ai/?models=glm-5-3-flash%2Cgemini... [1] Output Tokens Per Task - https://artificialanalysis.ai/?models=glm-5-3-flash%2Cgemini-3-7-flash%2Cgpt-5-6-terra%2Cgpt-5-6-sol-medium%2Cgpt-5-6-sol-high%2Cgpt-5-6-luna%2Cdeepseek-v4-flash%2Cgemini-3-7-flash-medium&intelligence-efficiency=output-tokens-per-task&intelligence-comparison=intelligence-vs-output-tokens-per-task#intelligence-efficiency-tabs https://artificialanalysis.ai/?models=glm-5-3-flash%2Cgemini... [2] https://deepswe.datacurve.ai/ https://deepswe.datacurve.ai/