GLM-5.3-Flash

rahimnathwani

Related: https://news.ycombinator.com/item?id=49446422 (281 points, 118 comments)

iamsyr

Standard API Pricing for GLM-5.3-Flash (per 1M tokens) - Input: $0.15 - Output: $0.50 - Cached input: $0.03

epolanski

I'm starting to think that this whole sanctioning China may motivate and prompt them to do more and better in every field. It's too big, bright and resourceful of a country to choose confrontation instead of collaboration.

sunbum

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

mmastrac

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 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.

packetlost

For those who didn't read, this is the identity of the mysterious "Ox Alpha" model

revolvingthrow

> 320B total parameters and just 18B active parameters 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.

Destiner

from the article, pareto frontier for open source models is completely dominated by GLM now.

TaLiTr

> it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. 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.

mariopt

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

garo-pro

> Combined with our latest 30T-token multimodal pre-training corpus [...] 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?

swingboy

How much is the “discounted” pricing they mention?

Imustaskforhelp

> To overcome the relatively limited compute and memory capacity of individual chips, we built a dedicated inference engine for this architecture on top of SGLang. Notably, this effort was accelerated by our GLM-5.3-powered infrastructure agent, which assisted engineers in developing and optimizing kernels, diagnosing performance bottlenecks, and improving the serving stack — creating a feedback loop in which the model helped optimize the system serving the model itself. > (...) 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.

toppy

By clicking this link you download some PDF in the background

kayleykiwi

This looks like it goes hard, can't wait to try it

yipinwong

When reading this type of announcements, always have keen eyes on graphs. 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)

cootsnuck

If we fast forward say 5 years, I don't see how we don't end up in world where people (and enterprises) are more savvy with how they use LLMs. Meaning, more models, smaller models, weirder models, more specialized models, etc. And all of it running on a variety of hardware (edge devices, personal computers, on-demand cloud compute). 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.

tokai

Why is their own coding plan always the last place z.ai release their models? Its even online, you just have to guess the model settings.

mrngld

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. 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!

AnodicElegy

Artificial Analysis benchmark is out: https://news.ycombinator.com/item?id=49450353