8 ms·

GLM-5.2 is a step change for open agents

367 points by vantareed 3mo ago

Balinares 3mo ago
I can't help wondering what kind of models we'll see coming out of China once it gets its own chip fabs up and running. Right now it sounds like the US's export ban is not slowing them down a whole lot.
- ceejayoz 3mo ago
  > Right now it sounds like the US's export ban is not slowing them down a whole lot. It may wind up being a massive boost to them in the long run, even. Necessity is the mother of invention.
  - pkroll 3mo ago
    If this pans out, you're not at all kidding: https://www.youtube.com/watch?v=8ekndZwyOzo https://www.youtube.com/watch?v=8ekndZwyOzo
  - verdverm 3mo ago
    Trump allowed more advanced chips (H200s) to be sold after his visit, because some people in the admin still believe the US can "addict" China to the hardware. It seems China is only letting a token few in, the ban is more on their side now, as Xi really wants indiginous capability.
- briga 3mo ago
  With subsidization from the Chinese government they will probably be equal to or better than the models here. I mean, have you looked at the author list of any given AI paper published within, say, the past 5 years? I wouldn't be surprised if half or more AI researches are from China.
  - buzzin__ 3mo ago
    Can you compare the amount to the USA subsidization? Which one is bigger? Per Capita? Per unit of economic growth achieved?
    - usef- 3mo ago
      You mean from the private investors? It seems the labs on both sides of the ocean are quite negative in their profitability right now due to the competitiveness. Though Anthropic claims they will have a profitable quarter this year (despite the huge build-out), so their margins on API costs are likely quite decent.
- pianopatrick 3mo ago
  There does not seem to be a big penalty for going slow anyways. People seem to just switch on cost as soon as a model can do a task well enough. There do not seem to be strong network effects or vendor lock in. Seems to me that going slow is the better long term tactic. China can just let the USA pay the high R&D costs to figure out what works, then just copy what works.
- khurs 3mo ago
  >Right now it sounds like the US's export ban is not slowing them down a whole lot. Just costing them a lot more money as they pay multiples more buying on the underground grey market.
jerojero 3mo ago
Open weight models from Chinese labs tend to be significantly cheaper. I think theyre absolutely needed. I can't afford 200 USD a month for personal use of coding AI, and I don't think such prices are reasonable for most of the world economy anyway. Not to mention US firms might be giving their employees a lot more than that. It's increasingly feeling, to me, that theres a gap building up between haves and have nots. But then, we get news of these open weight models that are reasonably priced in inference with reasonable capabilities. Yes, they take maybe 6-9 months to get there, tbh, that's not a bad trade off at all.
- narrator 3mo ago
  The tokens cost the same everywhere on earth. This does hurt some cost advantages of outsourcing when tokens start to become a bigger part of development costs.
- cameldrv 3mo ago
  Yes, but you’re paying with your data unless you’re hosting with a provider you trust or self-hosting.
  - sixothree 3mo ago
    My first instinct has been - well this is an open source project, what does it matter. But even then, I am guessing that using their service even for open source projects still provides them some value.
  - cookiengineer 3mo ago
    Kind of funny that you're assuming that you are not paying with your data in both cases. Do I need to remind you how LLMs are being trained? ...or that Anthropic claimed their codebase is 100% vibecoded, making it uncopyrightable by their own logic? ...or that Anthropic took down all Claude Code leaks they could find using DMCA takedown notices? ...or how do you think the caching mechanisms work when there's allegedly no data stored to be able to cache it? I'm just saying. Anything you build with online models is their training data anyways. Assuming otherwise is pretty stupid at this point.
themgt 3mo ago
I just tested GLM 5.2 out via Z.ai in pi for a little one-off project that was already scoped. It actually did a relatively decent job starting out, and figured important things out from context. But the reasoning traces became increasingly hilarious, with it getting confused and going in loops, doubting itself. I began to feel almost sad, it was like listening to the internal monologue of someone with anxiety disorder. It made pretty good progress but wound up going in a lot of goofy loops and doing things a bit "off" from standards I'd hoped it would infer, and finally started going a bit nuts, "This is very confusing.", "OH WAIT", seemingly hallucinating a whole side-quest that didn't make sense and looking at making internal system changes to try to achieve its (now very confused) goal when I pulled the plug. Without seeing the reasoning traces from Claude/GPT it's hard to really know, but it definitely didn't feel like the same quality of reasoning, even if dogged persistence does wind up actually working eventually.
- jauntywundrkind 3mo ago
  I think the self-doubt might actually be a very crucial part of it's capability. I often feel compelled to interrupt when I'm watching it think (which thank the stars it let's us do, unlike the big American models!!), but usually it makes the right pick! Being willing and able to reconsider seems very good. Going around and around, pulling in more thinking, integrating it: maybe that's why it is as good as it's good. I want to emphasize again how excellent it is that we can see the thinking. I think this makes GLM so much better an experience for me. It gives me such insight into what is being considered, helps me see where things go wrong. It grounds me, gives me the notion of where the results come from. It was so jarring to switch to GPT and Opus and find that they won't discuss with me, won't reveal their thinking: that feels fundamentally unsafe, for me, for society, to have such a severe black box. I don't think it should be allowed, honestly. Many thanks to this recent submission, which is the first time I've seen anyone blog about this core difference: The text in Claude Code’s “Extended Thinking” output is not authentic. https://patrickmccanna.net/the-text-in-claude-codes-extended-thinking-output-is-not-authentic/ https://patrickmccanna.net/the-text-in-claude-codes-extended... https://news.ycombinator.com/item?id=48630535 https://news.ycombinator.com/item?id=48630535
  - 3mo ago
citizenpaul 3mo ago
Ive been using glm5 since its release and still prefer it to glm5.1 and so far to glm5.2 Perhaps it is just my harness and workflow, but the older model still seems to work better. Also the token cost is significantly lower. I rarely spend more than $20 a week with $50 cap. Not even half claudes ambiguous minimum $200 a month plan.
- rainmaking 3mo ago
  Now that's a tremendous pointer, I'm going to have to try that. Do you full on let GLM5 get stuff done on its own or is it more like a guided workflow? The former's what the point releases doubled down on and is also something that uses a lot of juice.
  - citizenpaul 3mo ago
    Ive been using openspec and let it do the whole spected out project until its done. I dont interact other than the initial proposal, then apply, archive steps.I often run several in parallel on the same project in opencode.
timcobb 3mo ago
Can people share their GLM and open model setups in general please? What provider do you use. Why do you trust it with serving full quality? What harness do you use? Why do you trust it not to have malware (most harnessed are TS apps). I am just trying GLM 5.1 from Nvidia build in open code would love to hear how you all do it, thanks.
- rainmaking 3mo ago
  GLM 5.2 coding plan- I'll post the agent as soon as I can! But opencode works and their own zcode is really good as well.
- smoe 3mo ago
  For work, I mostly use Codex and some Claude. For personal use, I’ve started using Chinese models directly through their respective providers, mostly for automation tasks and experiments so far, either via the API directly or through the Pi harness. I do not trust any of them. Everything runs inside virtual machines, not just the sandboxes provided by the harnesses. I also do not run Claude or Codex directly on the host machine. Not just because of supply chain fears, but also because of how incredibly user hostile the VC funded companies are when it comes to installing random stuff on your machine.
- michimagdesign 3mo ago
  Next to my Claude Pro plan, I have subbed to OpenCode Go. I find the OpenCode UX much better than in Claude Code CLI. As for models, I started a few months ago with GLM 5.1 and it was solid and could archive near sonnet-level tasks. It weirdly sputtered out Chinese characters sometimes. Then I switched to Kimi K2.6, which is the Chinese model I used the most until now. It used way too many reasoning tokens (improved in k2.7). But executed Claude created plans reliably. Now I’m back with GLM 5.2 and it’s really solid (among other things it’s good at design) and I get good usage with the $10 plan. Still the Claude models have less hiccups but the Chinese models are getting really close.
  - mark_l_watson 3mo ago
    OpenCode Go looked intriguing and I spent time reading their docs and pricing but didn’t purchase services. Do you think they are running it at a loss to get market share? (Probably not.) I have been happy buying tokens directly from DeepSeek (I am retired and everything I do is open source code and writing open content books (the manuscript files are available along with the source code) so I have no privacy issues). I also use FireWorks.ai to try different models. Both API services are excellent, but I may try OpenCode Go for a month or two to support the devs of OpenCode.
aunty_helen 3mo ago
I signed up to a z.ai max account, $144. Hardly been able to use it as it 429s on most requests. They’re also refusing to refund me.
- sergiotapia 3mo ago
  My experience as well unfortunately :(
- osti 3mo ago
  Even as a GLM z.ai fan, I wouldn't pay for their plans. They are just way worse values than gpt or anthropic plans, in terms of both usage and capabilities.
- guybedo 3mo ago
  same here. Barely usable due to API connections issues. And when i can use it, it just drains the quota 5 times faster than codex or claude. Their plan is a scam
- fartcoin67 3mo ago
  [dead]
- reissbaker 3mo ago
  Self-promo but you should try our service synthetic.new. We generally have up-to-date open-source LLMs on the sub, and we have GLM-5.2 :) Perf+stability should be wayyy better than zai.
  - dotancohen 3mo ago
    What do you do differently that you expect to have better performance than an experienced, established player?
    - mtlynch 3mo ago
      Not GP, but just being smaller makes it easier to achieve reliability. Like if you're a git forge with 100 similar customers, you can likely achieve an order of magnitude better reliability than GitHub, who is trying to serve millions of customers with wildly different needs.
neosat 3mo ago
I've been using GLM 5.2 recently (company hosted, for non-coding tasks) and it's been strong and reliable. There are areas where GPT 5.5 and Opus 4.x still feel marginally better but only marginally. For most tasks if GLM 5.2 is the only model I have to use I'm productive and happy. This was not true before GLM 5.2. No doubt in my mind that the gap is closing quickly and for most tasks that are not very specialized open models will be usably on par on flagship closed models and have an edge factoring in cost. For coding I still use 5.5 w/ Codex and prefer that to other models + harness combinations.
fraywing 3mo ago
It feels like the gap is closing from an intelligence perspective. Or at least doing some kind of log flattening. Been playing with GLM 5.2 in different contexts. It's less good if you don't max out thinking, but as xhigh it's been able to solve most problems I was throwing at Opus in the about the same amount of time (via OpenRouter). Wild time to be alive.
- JSR_FDED 3mo ago
  Anecdote, not “research”: Yesterday I compared Deepseek, Kimi 2.6, MiMo 2.5 and GLM 5.2 for the same task (replace a custom token-based auth scheme with a cookies-based scheme across a front- and back-end codebase). I used Opencode with the zen subscription to try different models. All did this perfectly, basically indistinguishable from each other. However, when I pointed out that the new cookies-based auth didn’t allow multiple independent logins across browser tabs (which the previous scheme did allow) I noticed this: Deepseek, Kimi, MiMo started giving me multiple options but advocating strongly that I should either accept this deficiency, or don’t use the cookies version (keep the old auth scheme). They were so similar it was as if they were all the same model. Only GLM 5.2 said “here’s how to use cookies and also have tab-level separation”. The difference vs the other models was very stark.
christophilus 3mo ago
I've been working with Deepseek V4 Flash (with opencode as the harness). It's been almost indistinguishable from Codex / Claude Code for me. I'm sure I'll run into problems when I get to a stickier ticket to tackle. But so far, it's been quite good, and I find it writes straightforward code. I do think the Chinese models are good enough for an 80/20 rule use case.
- scottchiefbaker 3mo ago
  I tried Deepseek V4 Flash with very low expectations and was pleasantly surprised. It's a surprisingly capable model for the price.
  - timcobb 3mo ago
    What provider(s) do you use?
    - saaspirant 3mo ago
      Not op but I use their official platform. Cheapest token top-up is $2.12
- nunodonato 3mo ago
  it would be a really great option if it didn't lack vision
  - cromka 3mo ago
    For coding?
    - nunodonato 3mo ago
      reading pdfs
  - RugnirViking 3mo ago
    what do you use vision for? I have failed to find a workflow with it that makes sense, asking it to review screenshots of websites or whatever it misses extremely obvious details like text flowing out of it's container/overlapping other text, things being in entirely the wrong place, etc.
guybedo 3mo ago
GLM-5.2 has been a step change in how fast i can burn through tokens. I subscribed to their max plan to try it out. It counted me 700M tokens and drained my weekly quota in under 2 days. Quota just reset less than 24h ago and i'm already >60% weekly quota usage. For reference the kind of work i did would have used somewhere between 3% and 5% of Codex max or Claude max. The model is good, the plan is a scam
- jubilanti 3mo ago
  > The model is good, the plan is a scam If it is needing to generate that many tokens to do the same tasks, then it probably has higher inference costs. So (for you) the model is bad, the plan is the same plan.
- anatoliikmt 3mo ago
  What kind of tasks have you been using it for?
- try-working 3mo ago
  Kimi and GLM models have coined a new term: Thinkslop. They run a chain of thought that is up to 10x longer than other models and it seems that through a lookback mechanism they are able to use the CoT to reason about solutions to tasks they couldn't otherwise solve. The downside is of course that they consume many more tokens off your plan, and also that they are significantly slower. Kimi K2.7 takes about 7x longer to finish the same benchmark tasks as DeepSeek V4 Pro on my router benchmarks (https://role-model.dev/ https://role-model.dev/). So for now I'm happy with just two models: GPT and DeepSeek.
  - guybedo 3mo ago
    yeah Kimi K2.7 was doing ok but was painfully slow. The coding plan limits were good though. I haven't tried deepseek yet, i should check this one out.
    - try-working 3mo ago
      After the release of K2.7, the Kimi plan quotas have been reduced by about 80%.
dools 3mo ago
Is z.ai Is 2 better than x.ai
seany 3mo ago
What's the current best for ablation? Specifically chemistry and red-team/netsec?
- forsalebypwner 3mo ago
  ime DeepSeek v4 Pro is great for cybersec/netsec, I have not tried GLM though
mlmonkey 3mo ago
Here are the numbers from their bar chart: 1. SWE-bench Pro Model Score (%) GLM-5.2 62.1 GLM-5.1 58.4 Claude Opus 4.8 69.2 GPT-5.5 58.6 Gemini 3.1 Pro 54.2 2. Terminal-Bench 2.1 Model Score (%) GLM-5.2 81.0 GLM-5.1 63.5 Claude Opus 4.8 85.0 GPT-5.5 84.0 Gemini 3.1 Pro 74.0 3. NL2Repo Model Score (%) GLM-5.2 48.9 GLM-5.1 42.7 Claude Opus 4.8 69.7 GPT-5.5 50.7 Gemini 3.1 Pro 33.4 4. DeepSWE Model Score (%) GLM-5.2 46.2 GLM-5.1 18.0 Claude Opus 4.8 58.0 GPT-5.5 70.0 Gemini 3.1 Pro 10.0 5. ProgramBench Model Score (%) GLM-5.2 63.7 GLM-5.1 50.9 Claude Opus 4.8 71.9 GPT-5.5 70.8 Gemini 3.1 Pro 39.5 6. MCP-Atlas Model Score (%) GLM-5.2 77.0 GLM-5.1 71.8 Claude Opus 4.8 77.8 GPT-5.5 75.3 Gemini 3.1 Pro 69.2 7. Tool-Decathlon Model Score (%) GLM-5.2 48.2 GLM-5.1 40.7 Claude Opus 4.8 59.9 GPT-5.5 55.6 Gemini 3.1 Pro 48.8 8. Humanity's Last Exam Model Base Score (%) Score w/ Tools (%) GLM-5.2 40.5 54.7 GLM-5.1 31.0 52.3 Claude Opus 4.8 49.8 57.9 GPT-5.5 41.4 52.2 Gemini 3.1 Pro 45.0 51.4 Seems to be handily beating Gemini 3.1 Pro. What _is_ Google DeepMind doing (other than bleeding talent to A\ ) ?
- verdverm 3mo ago
  copying the graphs and tables to HN is noisy and harder to read
  - JSR_FDED 3mo ago
    Still more helpful than this comment
- vineyardmike 3mo ago
  > What _is_ Google DeepMind doing I feel like it has been pretty visible about what’s happening, between their press and products and financial statements. It’s just not what people are accustomed to expect. First, Google has become a major compute provider for competitors, thanks to TPUs. They’ve talked about allocating TPUs to GCP instead of their first party products. I can only assume it’s because they’re collecting a higher margin, and it covers the cost of data center buildout - which they’ve been aggressively doing. I wouldn’t be surprised if they made the financial decisions to delay or slow training for Gemini 3.5 when they provided last minute compute to Anthropic this spring. Second, Gemini has very directly not been focused on agentic coding, maybe 3.5 Flash being the change. They’ve built models they can deploy to watch YouTube videos, Nest cameras, scale to AI in search, understand fitness info in Fitbit, etc. They’re very clearly not focused around agentic/coding. They’ve put in a ton of efforts into multimodal data in and out, and they’re the only major lab working on video generation still. There was leak/rumor that their cofounder (brin) was getting involved in the model training to renew focus on agents so maybe this will change, and again 3.5 already feels different.
newaccountman2 3mo ago
5.1 and Qwen 3.6 are great too IMO
modgate 3mo ago
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yogthos 3mo ago
It's by far the most competent open model I've tried yet. It's a bit slower than Claude, but in terms of coding capability it seems to get comparable results at least for the work I'm doing.
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ideaxiaoshi 3mo ago
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sibellavia 3mo ago
While I agree with the post in its entirety, I think it would have been worth mentioning DeepSeek V4 Flash as well, which, in my view, had already reached a sufficient, if not high-level of agentic coding before GLM 5.2 (see DwarfStar).
nullbio 3mo ago
The idea of an open-weight Mythos model is not scary at all. This space is moving so quickly that it'll looked at in 1-2 years as childs play.
- Zopieux 3mo ago
  I don't understand those takes. Open-weights perhaps, but definitely not self-hostable – since those require $20k+ capex – which is the real "step change" to me, as it ends the stranglehold providers have over censorship. The only silver lining would be increased competition in API providers of those open-weight models leading to truly affordable prices and a race to remove stupid "safety" checks.
ramon156 3mo ago
I know very little about the current state of replacability of Opus but I do sometimes imagine a reality where Opus has been rebuilt as an open model. What plan does Anthropic have when it does happen? Will they still rent out their own model, will they support the open model and become a resource provider? Will they be able to repay the billions of dollars ? This is probably the first question I would ask someone from Anthropic, if I ever meet one.
- alpineman 3mo ago
  Did you read the article? Opus 4.5 has essentially been rebuilt already
  - mrngld 3mo ago
    Based on DeepSWE, Opus 4.8 gets you more intelligent output at lower price (GLM's token inefficiency is really biting them). GPT5.5 even moreso. And I don't recall about Opus but GPT is much, much faster at getting you the answer (again, GLM's token inefficiency). It's neat, I guess, that we can compare them against models released last year, but I care about my options today, and the pareto frontier is about as far away as it ever was. Add on top of that the extra features OpenAI and Anthropic have in their apps and...
    - alpineman 3mo ago
      As per the article, they are now about 6 months behind US frontier models, that's down from 9 months. The gap is closing
- olmo23 3mo ago
  > Will they still rent out their own model, will they support the open model and become a resource provider? Anthropic rents GPUs from xAI to run Claude. If there's an open weights competitor to Opus, why wouldn't Elon host it directly?
NovaCode37 3mo ago
Honestly, glm is staying quiet close to claude but it can save tons of tokens either than anthropic model
alfiedotwtf 3mo ago
Once open Chinese models look like they’re about to overtake closed US models, watch the US government push imperialism hidden behind increasingly hyperbolic national security concerns. At the end of the day, open weights should be seen as nothing more than information (just more just numbers afterall), and so organisations like the EFF should sue for any restricting of the 1st Amendment
s_kazmi 3mo ago
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melodyogonna 3mo ago
American AI labs really need to start releasing good open-weight models.
- fabijanbajo 3mo ago
  Agreed. Even just distilled versions of their frontier models would be a huge win for the open ecosystem
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bugthesystem 3mo ago
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nubg 3mo ago
A question I always have is, how to the AI labs safeguard the leak of their model? Training a cutting edge model basically cost a minimum of hundreds of millions of dollars. And its all contained within a file. Okay, that file might be 500GB large, but its still just one blob that is worth almost a billion dollars. And they need to train new models every few weeks, have lots of people with access to it to debug it, run inference etc. I wonder when we will see the first leaks? Imagine if e.g. Opus 4.8 got leaked. Wouldnt that bankrupt Anthropic?
GL26 3mo ago
if someone has any tutorial on how to run GLM-5.2 from a Rasberry Pi 5 (AI hat), I want it !
- efficax 3mo ago
  GLM-5.2 is a huge model. I don't think it would fit on the AI HAT+ 2 even if you quantized it to 2 bits
- adrian_b 3mo ago
  The full GLM-5.2 has 1.4 terabytes of data. Even in quantized form, reducing that size by 2 times or 4 times, a Raspberry Pi 5 could not run it otherwise than by reading the weights from an SSD. Even thus, I do not believe that a Raspberry Pi 5 would be fast enough to be able to run inference on such a big model at the speed at which it can read from the SSD. On the other hand, there are many mini-PCs with Intel or AMD CPUs that have both a PCIe 5.0 SSD and a PCIe 4.0 SSD, which may be read in parallel, achieving thus a reading throughput of up to 20 Gbyte/s. Such miniPCs have fast enough CPUs/GPUs, so that they might be able to reach the inference speed limited by a 20 Gbyte/s weight reading throughput, which for a so big model like GLM-5.2 would be of one output token every few seconds (only a fraction of the weights must be read for one output token). The ratio between output tokens per second and the weight reading throughput can be improved by various methods, like multi-token prediction or batching multiple tasks. Optimizing inference speed in such conditions is an active research subject, due to the high current memory prices.
ddemian 3mo ago
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geye1234 3mo ago
Curious to hear if anyone has tried running the 2-bit or 3-bit quantization of this. With a bit of investment I may just be able to swing it locally. I already have 96GB VRAM, so with 192GB RAM, which seems to be the most one can find these days with a 4-slot motherboard, I may be in with a shot. Yes, it'd be slow, but I could give it overnight jobs. But I don't know if running at such a low quantization would make it hallucinate with only a small context. Qwen and Gemma are great, but they need babysitting every 30 mins, which is quite a cognitive load.
- Farmadupe 3mo ago
  2 and 3 bit quants are often closer to gibberish than hallucination, and that'll happen regardless of context. I shouldn't claim too much, I haven't tried GLM5.2 at 2/3 bit quantization, but if I were a betting man I'd put money on "useless even as a chatbot"

367 points by vantareed 3mo ago

I can't help wondering what kind of models we'll see coming out of China once it gets its own chip fabs up and running. Right now it sounds like the US's export ban is not slowing them down a whole lot.

ceejayoz 3mo ago
> Right now it sounds like the US's export ban is not slowing them down a whole lot. It may wind up being a massive boost to them in the long run, even. Necessity is the mother of invention.
- pkroll 3mo ago
  If this pans out, you're not at all kidding: https://www.youtube.com/watch?v=8ekndZwyOzo https://www.youtube.com/watch?v=8ekndZwyOzo
- verdverm 3mo ago
  Trump allowed more advanced chips (H200s) to be sold after his visit, because some people in the admin still believe the US can "addict" China to the hardware. It seems China is only letting a token few in, the ban is more on their side now, as Xi really wants indiginous capability.
briga 3mo ago
With subsidization from the Chinese government they will probably be equal to or better than the models here. I mean, have you looked at the author list of any given AI paper published within, say, the past 5 years? I wouldn't be surprised if half or more AI researches are from China.
- buzzin__ 3mo ago
  Can you compare the amount to the USA subsidization? Which one is bigger? Per Capita? Per unit of economic growth achieved?
  - usef- 3mo ago
    You mean from the private investors? It seems the labs on both sides of the ocean are quite negative in their profitability right now due to the competitiveness. Though Anthropic claims they will have a profitable quarter this year (despite the huge build-out), so their margins on API costs are likely quite decent.
pianopatrick 3mo ago
There does not seem to be a big penalty for going slow anyways. People seem to just switch on cost as soon as a model can do a task well enough. There do not seem to be strong network effects or vendor lock in. Seems to me that going slow is the better long term tactic. China can just let the USA pay the high R&D costs to figure out what works, then just copy what works.
khurs 3mo ago
>Right now it sounds like the US's export ban is not slowing them down a whole lot. Just costing them a lot more money as they pay multiples more buying on the underground grey market.

jerojero 3mo ago

Open weight models from Chinese labs tend to be significantly cheaper. I think theyre absolutely needed. I can't afford 200 USD a month for personal use of coding AI, and I don't think such prices are reasonable for most of the world economy anyway. Not to mention US firms might be giving their employees a lot more than that. It's increasingly feeling, to me, that theres a gap building up between haves and have nots. But then, we get news of these open weight models that are reasonably priced in inference with reasonable capabilities. Yes, they take maybe 6-9 months to get there, tbh, that's not a bad trade off at all.

narrator 3mo ago
The tokens cost the same everywhere on earth. This does hurt some cost advantages of outsourcing when tokens start to become a bigger part of development costs.
cameldrv 3mo ago
Yes, but you’re paying with your data unless you’re hosting with a provider you trust or self-hosting.
- sixothree 3mo ago
  My first instinct has been - well this is an open source project, what does it matter. But even then, I am guessing that using their service even for open source projects still provides them some value.
- cookiengineer 3mo ago
  Kind of funny that you're assuming that you are not paying with your data in both cases. Do I need to remind you how LLMs are being trained? ...or that Anthropic claimed their codebase is 100% vibecoded, making it uncopyrightable by their own logic? ...or that Anthropic took down all Claude Code leaks they could find using DMCA takedown notices? ...or how do you think the caching mechanisms work when there's allegedly no data stored to be able to cache it? I'm just saying. Anything you build with online models is their training data anyways. Assuming otherwise is pretty stupid at this point.

themgt 3mo ago

I just tested GLM 5.2 out via Z.ai in pi for a little one-off project that was already scoped. It actually did a relatively decent job starting out, and figured important things out from context. But the reasoning traces became increasingly hilarious, with it getting confused and going in loops, doubting itself. I began to feel almost sad, it was like listening to the internal monologue of someone with anxiety disorder. It made pretty good progress but wound up going in a lot of goofy loops and doing things a bit "off" from standards I'd hoped it would infer, and finally started going a bit nuts, "This is very confusing.", "OH WAIT", seemingly hallucinating a whole side-quest that didn't make sense and looking at making internal system changes to try to achieve its (now very confused) goal when I pulled the plug. Without seeing the reasoning traces from Claude/GPT it's hard to really know, but it definitely didn't feel like the same quality of reasoning, even if dogged persistence does wind up actually working eventually.

jauntywundrkind 3mo ago
I think the self-doubt might actually be a very crucial part of it's capability. I often feel compelled to interrupt when I'm watching it think (which thank the stars it let's us do, unlike the big American models!!), but usually it makes the right pick! Being willing and able to reconsider seems very good. Going around and around, pulling in more thinking, integrating it: maybe that's why it is as good as it's good. I want to emphasize again how excellent it is that we can see the thinking. I think this makes GLM so much better an experience for me. It gives me such insight into what is being considered, helps me see where things go wrong. It grounds me, gives me the notion of where the results come from. It was so jarring to switch to GPT and Opus and find that they won't discuss with me, won't reveal their thinking: that feels fundamentally unsafe, for me, for society, to have such a severe black box. I don't think it should be allowed, honestly. Many thanks to this recent submission, which is the first time I've seen anyone blog about this core difference: The text in Claude Code’s “Extended Thinking” output is not authentic. https://patrickmccanna.net/the-text-in-claude-codes-extended-thinking-output-is-not-authentic/ https://patrickmccanna.net/the-text-in-claude-codes-extended... https://news.ycombinator.com/item?id=48630535 https://news.ycombinator.com/item?id=48630535
- 3mo ago

citizenpaul 3mo ago

Ive been using glm5 since its release and still prefer it to glm5.1 and so far to glm5.2 Perhaps it is just my harness and workflow, but the older model still seems to work better. Also the token cost is significantly lower. I rarely spend more than $20 a week with $50 cap. Not even half claudes ambiguous minimum $200 a month plan.

rainmaking 3mo ago
Now that's a tremendous pointer, I'm going to have to try that. Do you full on let GLM5 get stuff done on its own or is it more like a guided workflow? The former's what the point releases doubled down on and is also something that uses a lot of juice.
- citizenpaul 3mo ago
  Ive been using openspec and let it do the whole spected out project until its done. I dont interact other than the initial proposal, then apply, archive steps.I often run several in parallel on the same project in opencode.

timcobb 3mo ago

Can people share their GLM and open model setups in general please? What provider do you use. Why do you trust it with serving full quality? What harness do you use? Why do you trust it not to have malware (most harnessed are TS apps). I am just trying GLM 5.1 from Nvidia build in open code would love to hear how you all do it, thanks.

rainmaking 3mo ago
GLM 5.2 coding plan- I'll post the agent as soon as I can! But opencode works and their own zcode is really good as well.
smoe 3mo ago
For work, I mostly use Codex and some Claude. For personal use, I’ve started using Chinese models directly through their respective providers, mostly for automation tasks and experiments so far, either via the API directly or through the Pi harness. I do not trust any of them. Everything runs inside virtual machines, not just the sandboxes provided by the harnesses. I also do not run Claude or Codex directly on the host machine. Not just because of supply chain fears, but also because of how incredibly user hostile the VC funded companies are when it comes to installing random stuff on your machine.
michimagdesign 3mo ago
Next to my Claude Pro plan, I have subbed to OpenCode Go. I find the OpenCode UX much better than in Claude Code CLI. As for models, I started a few months ago with GLM 5.1 and it was solid and could archive near sonnet-level tasks. It weirdly sputtered out Chinese characters sometimes. Then I switched to Kimi K2.6, which is the Chinese model I used the most until now. It used way too many reasoning tokens (improved in k2.7). But executed Claude created plans reliably. Now I’m back with GLM 5.2 and it’s really solid (among other things it’s good at design) and I get good usage with the $10 plan. Still the Claude models have less hiccups but the Chinese models are getting really close.
- mark_l_watson 3mo ago
  OpenCode Go looked intriguing and I spent time reading their docs and pricing but didn’t purchase services. Do you think they are running it at a loss to get market share? (Probably not.) I have been happy buying tokens directly from DeepSeek (I am retired and everything I do is open source code and writing open content books (the manuscript files are available along with the source code) so I have no privacy issues). I also use FireWorks.ai to try different models. Both API services are excellent, but I may try OpenCode Go for a month or two to support the devs of OpenCode.

aunty_helen 3mo ago

I signed up to a z.ai max account, $144. Hardly been able to use it as it 429s on most requests. They’re also refusing to refund me.

sergiotapia 3mo ago
My experience as well unfortunately :(
osti 3mo ago
Even as a GLM z.ai fan, I wouldn't pay for their plans. They are just way worse values than gpt or anthropic plans, in terms of both usage and capabilities.
guybedo 3mo ago
same here. Barely usable due to API connections issues. And when i can use it, it just drains the quota 5 times faster than codex or claude. Their plan is a scam
fartcoin67 3mo ago
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reissbaker 3mo ago
Self-promo but you should try our service synthetic.new. We generally have up-to-date open-source LLMs on the sub, and we have GLM-5.2 :) Perf+stability should be wayyy better than zai.
- dotancohen 3mo ago
  What do you do differently that you expect to have better performance than an experienced, established player?
  - mtlynch 3mo ago
    Not GP, but just being smaller makes it easier to achieve reliability. Like if you're a git forge with 100 similar customers, you can likely achieve an order of magnitude better reliability than GitHub, who is trying to serve millions of customers with wildly different needs.

neosat 3mo ago

I've been using GLM 5.2 recently (company hosted, for non-coding tasks) and it's been strong and reliable. There are areas where GPT 5.5 and Opus 4.x still feel marginally better but only marginally. For most tasks if GLM 5.2 is the only model I have to use I'm productive and happy. This was not true before GLM 5.2. No doubt in my mind that the gap is closing quickly and for most tasks that are not very specialized open models will be usably on par on flagship closed models and have an edge factoring in cost. For coding I still use 5.5 w/ Codex and prefer that to other models + harness combinations.

fraywing 3mo ago

It feels like the gap is closing from an intelligence perspective. Or at least doing some kind of log flattening. Been playing with GLM 5.2 in different contexts. It's less good if you don't max out thinking, but as xhigh it's been able to solve most problems I was throwing at Opus in the about the same amount of time (via OpenRouter). Wild time to be alive.

JSR_FDED 3mo ago
Anecdote, not “research”: Yesterday I compared Deepseek, Kimi 2.6, MiMo 2.5 and GLM 5.2 for the same task (replace a custom token-based auth scheme with a cookies-based scheme across a front- and back-end codebase). I used Opencode with the zen subscription to try different models. All did this perfectly, basically indistinguishable from each other. However, when I pointed out that the new cookies-based auth didn’t allow multiple independent logins across browser tabs (which the previous scheme did allow) I noticed this: Deepseek, Kimi, MiMo started giving me multiple options but advocating strongly that I should either accept this deficiency, or don’t use the cookies version (keep the old auth scheme). They were so similar it was as if they were all the same model. Only GLM 5.2 said “here’s how to use cookies and also have tab-level separation”. The difference vs the other models was very stark.

christophilus 3mo ago

I've been working with Deepseek V4 Flash (with opencode as the harness). It's been almost indistinguishable from Codex / Claude Code for me. I'm sure I'll run into problems when I get to a stickier ticket to tackle. But so far, it's been quite good, and I find it writes straightforward code. I do think the Chinese models are good enough for an 80/20 rule use case.

scottchiefbaker 3mo ago
I tried Deepseek V4 Flash with very low expectations and was pleasantly surprised. It's a surprisingly capable model for the price.
- timcobb 3mo ago
  What provider(s) do you use?
  - saaspirant 3mo ago
    Not op but I use their official platform. Cheapest token top-up is $2.12
nunodonato 3mo ago
it would be a really great option if it didn't lack vision
- cromka 3mo ago
  For coding?
  - nunodonato 3mo ago
    reading pdfs
- RugnirViking 3mo ago
  what do you use vision for? I have failed to find a workflow with it that makes sense, asking it to review screenshots of websites or whatever it misses extremely obvious details like text flowing out of it's container/overlapping other text, things being in entirely the wrong place, etc.

guybedo 3mo ago

GLM-5.2 has been a step change in how fast i can burn through tokens. I subscribed to their max plan to try it out. It counted me 700M tokens and drained my weekly quota in under 2 days. Quota just reset less than 24h ago and i'm already >60% weekly quota usage. For reference the kind of work i did would have used somewhere between 3% and 5% of Codex max or Claude max. The model is good, the plan is a scam

jubilanti 3mo ago
> The model is good, the plan is a scam If it is needing to generate that many tokens to do the same tasks, then it probably has higher inference costs. So (for you) the model is bad, the plan is the same plan.
anatoliikmt 3mo ago
What kind of tasks have you been using it for?
try-working 3mo ago
Kimi and GLM models have coined a new term: Thinkslop. They run a chain of thought that is up to 10x longer than other models and it seems that through a lookback mechanism they are able to use the CoT to reason about solutions to tasks they couldn't otherwise solve. The downside is of course that they consume many more tokens off your plan, and also that they are significantly slower. Kimi K2.7 takes about 7x longer to finish the same benchmark tasks as DeepSeek V4 Pro on my router benchmarks (https://role-model.dev/ https://role-model.dev/). So for now I'm happy with just two models: GPT and DeepSeek.
- guybedo 3mo ago
  yeah Kimi K2.7 was doing ok but was painfully slow. The coding plan limits were good though. I haven't tried deepseek yet, i should check this one out.
  - try-working 3mo ago
    After the release of K2.7, the Kimi plan quotas have been reduced by about 80%.

dools 3mo ago

Is z.ai Is 2 better than x.ai

seany 3mo ago

What's the current best for ablation? Specifically chemistry and red-team/netsec?

forsalebypwner 3mo ago
ime DeepSeek v4 Pro is great for cybersec/netsec, I have not tried GLM though

mlmonkey 3mo ago

Here are the numbers from their bar chart: 1. SWE-bench Pro Model Score (%) GLM-5.2 62.1 GLM-5.1 58.4 Claude Opus 4.8 69.2 GPT-5.5 58.6 Gemini 3.1 Pro 54.2 2. Terminal-Bench 2.1 Model Score (%) GLM-5.2 81.0 GLM-5.1 63.5 Claude Opus 4.8 85.0 GPT-5.5 84.0 Gemini 3.1 Pro 74.0 3. NL2Repo Model Score (%) GLM-5.2 48.9 GLM-5.1 42.7 Claude Opus 4.8 69.7 GPT-5.5 50.7 Gemini 3.1 Pro 33.4 4. DeepSWE Model Score (%) GLM-5.2 46.2 GLM-5.1 18.0 Claude Opus 4.8 58.0 GPT-5.5 70.0 Gemini 3.1 Pro 10.0 5. ProgramBench Model Score (%) GLM-5.2 63.7 GLM-5.1 50.9 Claude Opus 4.8 71.9 GPT-5.5 70.8 Gemini 3.1 Pro 39.5 6. MCP-Atlas Model Score (%) GLM-5.2 77.0 GLM-5.1 71.8 Claude Opus 4.8 77.8 GPT-5.5 75.3 Gemini 3.1 Pro 69.2 7. Tool-Decathlon Model Score (%) GLM-5.2 48.2 GLM-5.1 40.7 Claude Opus 4.8 59.9 GPT-5.5 55.6 Gemini 3.1 Pro 48.8 8. Humanity's Last Exam Model Base Score (%) Score w/ Tools (%) GLM-5.2 40.5 54.7 GLM-5.1 31.0 52.3 Claude Opus 4.8 49.8 57.9 GPT-5.5 41.4 52.2 Gemini 3.1 Pro 45.0 51.4 Seems to be handily beating Gemini 3.1 Pro. What _is_ Google DeepMind doing (other than bleeding talent to A\ ) ?

verdverm 3mo ago
copying the graphs and tables to HN is noisy and harder to read
- JSR_FDED 3mo ago
  Still more helpful than this comment
vineyardmike 3mo ago
> What _is_ Google DeepMind doing I feel like it has been pretty visible about what’s happening, between their press and products and financial statements. It’s just not what people are accustomed to expect. First, Google has become a major compute provider for competitors, thanks to TPUs. They’ve talked about allocating TPUs to GCP instead of their first party products. I can only assume it’s because they’re collecting a higher margin, and it covers the cost of data center buildout - which they’ve been aggressively doing. I wouldn’t be surprised if they made the financial decisions to delay or slow training for Gemini 3.5 when they provided last minute compute to Anthropic this spring. Second, Gemini has very directly not been focused on agentic coding, maybe 3.5 Flash being the change. They’ve built models they can deploy to watch YouTube videos, Nest cameras, scale to AI in search, understand fitness info in Fitbit, etc. They’re very clearly not focused around agentic/coding. They’ve put in a ton of efforts into multimodal data in and out, and they’re the only major lab working on video generation still. There was leak/rumor that their cofounder (brin) was getting involved in the model training to renew focus on agents so maybe this will change, and again 3.5 already feels different.

newaccountman2 3mo ago

5.1 and Qwen 3.6 are great too IMO

modgate 3mo ago

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yogthos 3mo ago

It's by far the most competent open model I've tried yet. It's a bit slower than Claude, but in terms of coding capability it seems to get comparable results at least for the work I'm doing.

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ideaxiaoshi 3mo ago

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sibellavia 3mo ago

While I agree with the post in its entirety, I think it would have been worth mentioning DeepSeek V4 Flash as well, which, in my view, had already reached a sufficient, if not high-level of agentic coding before GLM 5.2 (see DwarfStar).

nullbio 3mo ago

The idea of an open-weight Mythos model is not scary at all. This space is moving so quickly that it'll looked at in 1-2 years as childs play.

Zopieux 3mo ago
I don't understand those takes. Open-weights perhaps, but definitely not self-hostable – since those require $20k+ capex – which is the real "step change" to me, as it ends the stranglehold providers have over censorship. The only silver lining would be increased competition in API providers of those open-weight models leading to truly affordable prices and a race to remove stupid "safety" checks.

ramon156 3mo ago

I know very little about the current state of replacability of Opus but I do sometimes imagine a reality where Opus has been rebuilt as an open model. What plan does Anthropic have when it does happen? Will they still rent out their own model, will they support the open model and become a resource provider? Will they be able to repay the billions of dollars ? This is probably the first question I would ask someone from Anthropic, if I ever meet one.

alpineman 3mo ago
Did you read the article? Opus 4.5 has essentially been rebuilt already
- mrngld 3mo ago
  Based on DeepSWE, Opus 4.8 gets you more intelligent output at lower price (GLM's token inefficiency is really biting them). GPT5.5 even moreso. And I don't recall about Opus but GPT is much, much faster at getting you the answer (again, GLM's token inefficiency). It's neat, I guess, that we can compare them against models released last year, but I care about my options today, and the pareto frontier is about as far away as it ever was. Add on top of that the extra features OpenAI and Anthropic have in their apps and...
  - alpineman 3mo ago
    As per the article, they are now about 6 months behind US frontier models, that's down from 9 months. The gap is closing
olmo23 3mo ago
> Will they still rent out their own model, will they support the open model and become a resource provider? Anthropic rents GPUs from xAI to run Claude. If there's an open weights competitor to Opus, why wouldn't Elon host it directly?

NovaCode37 3mo ago

Honestly, glm is staying quiet close to claude but it can save tons of tokens either than anthropic model

alfiedotwtf 3mo ago

Once open Chinese models look like they’re about to overtake closed US models, watch the US government push imperialism hidden behind increasingly hyperbolic national security concerns. At the end of the day, open weights should be seen as nothing more than information (just more just numbers afterall), and so organisations like the EFF should sue for any restricting of the 1st Amendment

s_kazmi 3mo ago

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melodyogonna 3mo ago

American AI labs really need to start releasing good open-weight models.

fabijanbajo 3mo ago
Agreed. Even just distilled versions of their frontier models would be a huge win for the open ecosystem

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bugthesystem 3mo ago

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nubg 3mo ago

A question I always have is, how to the AI labs safeguard the leak of their model? Training a cutting edge model basically cost a minimum of hundreds of millions of dollars. And its all contained within a file. Okay, that file might be 500GB large, but its still just one blob that is worth almost a billion dollars. And they need to train new models every few weeks, have lots of people with access to it to debug it, run inference etc. I wonder when we will see the first leaks? Imagine if e.g. Opus 4.8 got leaked. Wouldnt that bankrupt Anthropic?

GL26 3mo ago

if someone has any tutorial on how to run GLM-5.2 from a Rasberry Pi 5 (AI hat), I want it !

efficax 3mo ago
GLM-5.2 is a huge model. I don't think it would fit on the AI HAT+ 2 even if you quantized it to 2 bits
adrian_b 3mo ago
The full GLM-5.2 has 1.4 terabytes of data. Even in quantized form, reducing that size by 2 times or 4 times, a Raspberry Pi 5 could not run it otherwise than by reading the weights from an SSD. Even thus, I do not believe that a Raspberry Pi 5 would be fast enough to be able to run inference on such a big model at the speed at which it can read from the SSD. On the other hand, there are many mini-PCs with Intel or AMD CPUs that have both a PCIe 5.0 SSD and a PCIe 4.0 SSD, which may be read in parallel, achieving thus a reading throughput of up to 20 Gbyte/s. Such miniPCs have fast enough CPUs/GPUs, so that they might be able to reach the inference speed limited by a 20 Gbyte/s weight reading throughput, which for a so big model like GLM-5.2 would be of one output token every few seconds (only a fraction of the weights must be read for one output token). The ratio between output tokens per second and the weight reading throughput can be improved by various methods, like multi-token prediction or batching multiple tasks. Optimizing inference speed in such conditions is an active research subject, due to the high current memory prices.

ddemian 3mo ago

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geye1234 3mo ago

Curious to hear if anyone has tried running the 2-bit or 3-bit quantization of this. With a bit of investment I may just be able to swing it locally. I already have 96GB VRAM, so with 192GB RAM, which seems to be the most one can find these days with a 4-slot motherboard, I may be in with a shot. Yes, it'd be slow, but I could give it overnight jobs. But I don't know if running at such a low quantization would make it hallucinate with only a small context. Qwen and Gemma are great, but they need babysitting every 30 mins, which is quite a cognitive load.

Farmadupe 3mo ago
2 and 3 bit quants are often closer to gibberish than hallucination, and that'll happen regardless of context. I shouldn't claim too much, I haven't tried GLM5.2 at 2/3 bit quantization, but if I were a betting man I'd put money on "useless even as a chatbot"