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GLM-5.3-Flash

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Re: GLM-5.3-Flash

#271

Post by PhilippGille »

microtonal wrote: post #122
matheusmoreira wrote: post #95

You guys read Z.ai's terms of service, right? 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…

The model is MIT-licensed, so run it on any of the non-Chinese inference providers that will host it in a few days.

NovitaAI is already hosting it: https://openrouter.ai/z-ai/glm-5.3-flash#providers

Re: GLM-5.3-Flash

#273

Post by twobitshifter »

saberience wrote: post #226
mariopt wrote: post #20

It's only 320B, local frontier AI is getting closer, sooner than expected.

It's not possible to keep shrinking down parameters and keep "frontier" performance, it's like saying it's possible to take a 3 hour movie and compress it down to 3 megabytes, there are information theoretic limits on the amount of bits of information that can be compressed. 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 w…

The current models are not close to approaching the limit of compression for intelligence. They aren’t even focused on it like Chinese labs are. The training of Qwen’s 27B parameter model showed that by structuring model training from fundamentals to more difficult topics they were able to drastically reduce the number of parameters needed.

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.

Re: GLM-5.3-Flash

#274

Post by Jeremy1026 »

metadat wrote: post #168
mmastrac wrote: post #7

Weights on HF here: https://huggingface.co/zai-org/GLM-5.3-Flash 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 experiment…

Qwen 3.8 27B is around Opus 4.8 level of capability on the Agentic Intelligence Index (52 vs 57). In my testing the locally hosted Qwen is good enough that looking at a given piece of work output I couldn't tell you which model was behind it. https://artificialanalysis.ai/models/qwen3-8-27b?models=gpt-...

I gave Qwen 3.8 27B and Opus 4.8 the same task in the same codebase. They both came up with the same diff. It wasn't a particularly challenging task (removing a feature flag and updating applicable specs), but it was character for character.

Re: GLM-5.3-Flash

#275

Post by cheema33 »

bityard wrote: post #203
sunbum wrote: post #5

> with all of this traffic served on Chinese AI chips RIP Nivida shareholders

Most US companies that have anything to do with government, finance, medical, etc. already have contractual or regulatory obligations which prevent them from using Chinese hardware or services, even before the AI boom. That's a huge market. Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)

> Most US companies that have anything to do with government, finance, medical, etc... That's a huge market.

Compared to the rest of the world?

Re: GLM-5.3-Flash

#276

Post by KronisLV »

vehemenz wrote: post #130
swatcoder wrote: post #69

Earlier quoted context omitted.

Compare to the cost of professional-grade tools in other trades and craft hobbies. Sure, $4000 can be a lot of if you're a casual hobbyist or are struggle to meet everyday lifestyle costs, but it's definitely not "insane" if this is the trade you make your living from or if you've established a lifestyle that affords disposable income for your hobbies. And for some people, $4000 for a device you have complete control…

That's only half the reason it's expensive. The other reason is that it would likely take years to spend $4000 (plus the real cost of electricity) worth of tokens on a 3rd-party provider that's running a similar limited, DS Flash type model. By that time, the hardware will be obsolete, assuming it's still operational.

But if you can use cloud models, why wouldn’t you use SOTA? For 2400 USD or less per year you can get pretty huge amounts of benefit out of that (though at the whim of whoever you are giving the money to).

Re: GLM-5.3-Flash

#277

Post by throwaway240403 »

szundi wrote: post #232
skohan wrote: post #228

Earlier quoted context omitted.

I've had the exact opposite experience. I've been using 3.8 for my daily driver since last week, and I've gradually been giving it more and more complex tasks as it continues to deliver high quality results. Now I am basically handing off large complex features, and 3.8 is doing the planning, task breakdown, implementation and review with just a few notes from my side. The tradeoff is time (especially on RDMA4 hardwa…

Fascinating that after years of for me this for me that, literally no one writes 2-3 other words like “i do react frontend” or whatever just for us to know why the results are different

lol, exactly.

Re: GLM-5.3-Flash

#278

Post by pphysch »

polski-g wrote: post #246
claudeIsDown wrote: post #94

On OpenRouter the pricing is: Input $0,075/M - Output $0,25/M - Cache Read $0,015 /M How is the business model of Anthropic/OpenAI will sustain?

This is a bad model. Worse than Luna in every way; slower, dumber.

OAI/Anthropic shareholder? Speed and intelligence are not "every way". Cost is essential. Hence the Pareto boundary illustrated in TFA.

Re: GLM-5.3-Flash

#279

Post by zerd »

DetroitThrow wrote: post #225
metadat wrote: post #168

Earlier quoted context omitted.

Qwen 3.8 27B is around Opus 4.8 level of capability on the Agentic Intelligence Index (52 vs 57). In my testing the locally hosted Qwen is good enough that looking at a given piece of work output I couldn't tell you which model was behind it. https://artificialanalysis.ai/models/qwen3-8-27b?models=gpt-...

Lately I've been throwing tasks at Qwen and a frontier or recently-frontier model (as well as Kimi, GLM, etc) and the smaller parameter models are not really comparable to Opus when it comes to making intelligent decisions about greyer areas of good software architecture. 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…

I’ve been doing the same thing, giving the same tasks to Qwen 3.8 27B and Opus, and the main difference is that Qwen does not consider edge cases which Opus catches. It’s good at the happy path, but even when hinting that there are uncovered edge cases and gotchas it’s oblivious to it. So I feel like I need a bigger model to do planning/review.

Re: GLM-5.3-Flash

#280

Post by John7878781 »

glub wrote: post #185
mrngld wrote: post #79

Chinese labs are so used to manipulating benchmarks to try to flatter inferior models that when they finally have one that's really pretty good I think the official announcement here undersells it. https://deepswe.datacurve.ai/ 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…

I don't know how anyone can actually use Luna max on ANY real workload. I've had Sol orchestrate a bunch of Luna agents, these agents were explicitly given small chunks of larger objectives and they still filled their entire context windows with just reasoning tokens, until compaction hit, and then reasoning again. I've probably wasted a good 40% of my weekly usage on Luna Max agents just thinking and not writing a s…

Luna max is all I use. In my experience, it works really well for overnight tasks.
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