Looking at HF, it looks like the unquantized version is half the size of glm-5.2 756 GB vs 1.51 TB. I wonder how they were able to optimize it this well
GLM 5.3 is probably the sweet spot open weights model if you want to go beyond deepseek flash or the new glm flash. I used it with pi and had a fairly good time, especially since it’s less touchy about cyber and whatnot than the US guys. It’s slightly behind Kimi in ability but it’s a lot easier to run it, I’d expect prices (and speed!) from third parties to be noticeably better.
Assuming you’re willing to drop a fat stack of cash on the upcoming Mac m5 ultra with 512 gb unified memory, you can even run it locally, quantized to 4 bit. Whether it’s even slightly reasonable, well, my wife would probably skin me alive but maybe yours is more understanding.
* Models are evolving quickly with high worldwide competition
* Hardware is evolving despite RAM shortages
Is investing a huge sum of money in equipment for local inference a wise use of money? Or are M5 Ultra and equivalently priced local inference hardware future-proof enough to be worth it relative to how the market is evolving? Maybe it’s all a question of what you’d spend otherwise on serverless or dedicated GPU spend…
It is absolutely not worth buying hardware to run models for purely (long term) cost reasons. For open weights models the economies of scale means the cloud beats local significantly and your payback time is like 10 years.
However there are other reasons (e.g. privacy) that might make it worth running locally for some people.
I have just built an Epyc with 512gb DDR4 3200 RAM for a "reasonable" price and I'm hoping to have a setup with GLM as the architect and Qwen 27b/Next Flash as the implementer. This is 1/5 of the price of the Mac, but also probably 1/5 of the speed lol.
I’ll be very curious what you get with DDR4. I also almost went that way. I have an Epyc DDR 5 rig and the best I see is 10 tok/s. Caveat being that’s at Q8 and a 4090 doing pre fill so it could be pushed up.
The surprising thing for me is how much work you will need to cool the banks if you’re near your memory ceiling. My memory starts soft throttling at about 74C (dies may be hotter, that’s the bank temp) and will turn down speed to try to stay below 80.
Happy to send my llama.cpp config settings if you want it.
Honestly I suspect neither of them will be performing terribly well but with DDR4 3200 RAM I wonder if you'll be counting tokens per second or seconds per token. I mean, you do at least get a lot of memory channels at least, compared to consumer PCs. I am curious to hear what performance you get, I feel there is not enough information out there on what different setups manage to eek out.
The fastest I was able to get my Threadripper 3960X + 2x 3090s + 256GB DDR4-3200 to run a 2-bit quant of GLM-5.2 was 8 TPS. I would expect to be in seconds-per-token territory for a pure-CPU 4-bit quant.
One thing I'd like to try is MoE offloading: I have 2x32 GiB of VRAM and 128 GiB of DDR5 running at 4800 MT/s (only 2 channels though). I've seen people post difficult to believe MoE offloading results albeit a decently long time ago with older models. Maybe there is a quant that would fit with MoE offloading?
That said, I am guessing my problem is not enough RAM - but this poor consumer platform struggles to do memory training with 128 GiB as it is.
Now I surely regret not having gotten Threadripper and 256 GiB of RAM in the before-times.
My measurement was with MoE offloading, but there's only so much you can keep on-GPU with a 200GB quant and 48GB of VRAM. It's hard to overcome the CPU/RAM bottleneck.
For what it's worth, all of my hardware was used; I think, all-in, I'm probably at around 3k-4k USD? Not cheap, but also not the worst for something relatively versatile.
One could also run it locally on a used dual xeon (or amd-equivalent) server with 512GB RAM, albeit slower, if you have a useful workflow for it that's like "take this day's efforts and run it through various analysis agents", combined with giving it one-shot tasks/modules to build overnight. You would want a place like a garage or basement to put the server because it'll be loud.
Memory throughput performance is better than many equivalently priced (talking about the cheaper end of the used/refurb market here) single socket systems because each CPU is talking to its own banks of RAM.
Using an example of llama-server on a 256GB (each CPU is attached to 128GB) system running qwen 3.8-flash-next Q8, which takes up about 188GB with the model and context, it's not that the inference engine 'knows' about NUMA, but if you use --load-mode mlock to load the whole thing into memory at once at the time llama-server launches, the kernel will distribute it relatively evenly across the memory connected to both CPUs.
Well if you did get the m5 ultra could you obliterate the guardrails and then your wife can ask it pertinent but unsafe questions about how to punish you. Seems doable.
Oh please, as though significant numbers of tech workers on this website are not buying LV, Gucci, and yes Hermes for their pampered wives.
My wife can have an opinion on my tech purchases when I get an opinion on her 20,000$ bag addiction.
And the worst part is both of you should unironically accept these purchases, as somehow luxury bags, high end hardware, AND rolex's often out-pace inflation and are objectively good stores of value in a world of rapidly increasing income/wealth inequality.
I'd like to ask Sam Altman if he still thinks that it's too dangerous to publish GPT-3. I mean, no one would use it, but what is his reasoning for not publishing it now, in 2026?
I think it would be an important historical document as well. We are potentially looking at the dawn of AGI and one of the most important models ever created. Each model is also a kind of ultimate time capsule, containing a snapshot of the entire human collective mind. If you wanted to ask a 2002 person what they thought about future historical events you can just ask them directly.
There’s not such a straightforward relationship between safety and model sis.
According to the book The Thinking Game, lower quality models at that time were considered less safe, because they could be easily tricked into doing harmful stuff. In the book, Dario (of Anthropic) was the head of safety at openAI and was responsible for pushing for 10x scaling in training to make the models safer.
It does make sense, a smart model is going to be way harder to jailbreak into telling me how to synthesize anthrax (or something).
There are risks associated with releasing historical proprietary models that were not designed for open release:
- It is trivial to extract samples of the training data that was used, which can bolster existing lawsuits/foster new ones.
- Older models are not as safety-hardened, so it is easier to coax unsafe behaviour out of them, which is a PR risk.
- It may be possible to divulge proprietary secrets from the model (e.g. architectural details that may still be relevant).
For these reasons, and more, it's unlikely that GPT-3/similar models will be released until these concerns are no longer relevant (e.g. when they become a purely historic concern, similar to the open-sourcing of other proprietary software from decades ago).
Maybe I'm reading too much between the lines, but I suspect the reason is to rub his nose in the duplicity or naivety depending on how generous you're feeling. Publishing the model would be a confession that he was wrong.
AI policy is being shaped somewhat by the things Sam and Dario say. So even if you're not feeling vindictive, it's probably good to keep a track record of the previous things they have said as a Bayesian prior. People who don't know better listen to these people, and maybe they shouldn't.
If you want to dunk on sam for "it's too dangerous to publish GPT-3", you hardly need the release of gpt-3 to prove your point. All the other open model releases already provide pretty good evidence. Moreover the fact that the model wasn't release hardly points to the fact that he wanted to save face or whatever. Most AI labs don't release their old proprietary models, so the fact that gpt-3 wasn't released tells us very little.
I'm starting to think Opus 4.8 is significantly smaller than most people assume.
If it's significantly larger than GLM 5.3 (I've heard some insane guesstimates out there like upwards of 5T params or more), that would prove rather embarrassing for Anthropic.
I hear the argument here, but isn't it possible it has dramatically more knowledge and when you get outside the common cases many of us use it for, it'll have completely different capabilities?
I feel like most benchmarks cluster on a reasonably limited area of human knowledge
It seems like there is tradeoff between model size and the need for tool use, which - in my mind - is quite costly in terms of time and tokens.
More detailed world knowledge requires an exponential increase in model size, but most knowledge can be acquired ad hoc using search or database queries. This will fail for questions where the model lacks the knowledge to ask the right questions, but maybe this could be solved by a handful small inquiry models with different knowledge encoded in their weights?
You can't compare models released 6+ months apart. GLM 5.2 was same architecture as 5.3 and not nearly as good. Takes time to build frontier intelligence and distill down to smaller sizes.
Do you use it to write HTML/CSS? Javascript? C++? There's a huge difference in ways people use models and if you are not specific about it then your comment means nothing, unfortunately.
I really like how it doesn't have that Claude talk. It just does the thing without Claude's "load-bearing honesty." It's probably my favorite model to interact with, even if it isn't the best or most reliable.
My second favorite model by now is GLM 5.3 flash which is very capable of day to day task. I use it as the main model and GLM 5.3 for task that is more complex
z.ai coder plan, both in opencode and direct API access. I use it for my side projects like https://github.com/scosman/Biscotti (on-device meeting transcription and summaries).
I have been only using GLM models since last December and have had the best experience without any drama about tokens and geopolitical restrictions. The quality has been great and I am doing more and more with the latest 5.3 and am really excited that consumer hardware will develop in the next few years where I can run these at home.
Have you guys been having a good experience with OpenRouter? I tried it out recently with Claude, and it cached no tokens, charging me $200 for one conversation of 11 messages.
I previously posted that DS4Flash was _good_ but not _great_ on two DGX Sparks, but I have to say that GLM-5.3 is pretty amazing. It's been able to tackle all the random hard problems I've thrown at it and it has the intuition that DS4Flash seems to lack.
We're nowhere near a Fable-class model IMO, but things are going to get interesting in this next year.
how feasible is it build a SOTA specialized model for some use case e.g. deal sourcing by using this as pre-trained model or a LORA or similar pattern on top? Gonna shoot my shot at a billion dollar business
Is it possible to fine tune this model and unlock / extend its cybersecurity capabilities? I'm scared that maybe we are not ready for an open-weight model with high cybersecurity skills.
Not in my experience. Tasks that would normally cost $0.08 on DSV4-Flash have cost me $0.30+ on GLM-5.3-Flash. These costs are after Deepseek's recent increase. Also GLM-5.3-Flash is so slow compared to DSV4-Flash. I would be fine with GLM-5.3-Flash if it was cheaper and at the same speed as DSV4.
I use DSV4-Flash on Max through Deepseek's API. I have been using GLM-5.3-Flash on High through Openrouter which I thought had a 50% discount. I must be doing something wrong for the costs to be off this much.
It's actually slightly more expensive ($0.50 vs $0.48), but there's a temporary 50% discount.
I've seen dozens of conversations about it in last 24 hours, and every major inference provided added in first 24 hours. I think it's gaining plenty of traction.
OpenCode Go is probably using quantized down DS4Flash. They outsourced to 3th party providers to keep the cost down, and being able to provide that $30 value (instead of the initial $60 > $15).
We saw the same issue with GLM 5.2 when they still published publicly who the providers are on their website. Most ran FP8 but one was doing FP4, so you had this issue where one moment you had the better FP8 and another session you had the FP4 provider.
You can check the internet archive, it was in the FAQ part before they hide/removed it. So if you looked up the providers, and the published quants, yea, ...
Given that a lot of complaints are coming from people that felt OpenCode Go Flash feel like a step down compared to old OpenCode Go/DeepSeek API directly, it smells of a quantized down provider is mixed in.
OpenCode Go is becoming less of a good deal by the month. I pretty much only use it for mimo 2.5 pro now, and everything else is either ollama or openrouter.
I've been using it quite a bit too. My main complaint is that it can be really slow sometimes — like, really slow — and the speed feels pretty inconsistent.
How much usage do you find you get on these kinda models (I know the pricing changes a bit) compared to a $20 sub say for Google AI Pro in anti gravity?
I hate how difficult it is to compare prices when looking at subscriptions.
Would $20 in open router, using models like GLM get me more or less?
I think it'd get you less than a $20 sub to any of the big three. I've used it on OpenRouter and found it kind of expensive for the results, but that might change now that it's open weight and other providers can host it/compete with Z.ai. For the work I did with it, I would've rather used DeepSeek V4 Flash just because it's more economical and still gives good results IMO.
Z.ai does have their own subscription, but I haven't used it because their privacy policy was pretty buns last time I checked.
just tested (zai-org/GLM-5.3-Flash via together.ai) against latest DeepSeek-V4-Flash for a very specific task and thought i'd report here...
- price: DS4 wins... $0.0235 vs $0.0242 for ten tasks
- latency: GLM wins... 108s total against 154s
this is for a personal use-case where i'm detecting ads in a written transcript. sticking with ds4-flash for now since latency is not a critical factor
Looking at HF, it looks like the unquantized version is half the size of glm-5.2 756 GB vs 1.51 TB. I wonder how they were able to optimize it this well
GLM 5.3 is probably the sweet spot open weights model if you want to go beyond deepseek flash or the new glm flash. I used it with pi and had a fairly good time, especially since it’s less touchy about cyber and whatnot than the US guys. It’s slightly behind Kimi in ability but it’s a lot easier to run it, I’d expect prices (and speed!) from third parties to be noticeably better.
Assuming you’re willing to drop a fat stack of cash on the upcoming Mac m5 ultra with 512 gb unified memory, you can even run it locally, quantized to 4 bit. Whether it’s even slightly reasonable, well, my wife would probably skin me alive but maybe yours is more understanding.
When we consider:
* LLM usage is new for the world
* Models are evolving quickly with high worldwide competition
* Hardware is evolving despite RAM shortages
Is investing a huge sum of money in equipment for local inference a wise use of money? Or are M5 Ultra and equivalently priced local inference hardware future-proof enough to be worth it relative to how the market is evolving? Maybe it’s all a question of what you’d spend otherwise on serverless or dedicated GPU spend…
It is absolutely not worth buying hardware to run models for purely (long term) cost reasons. For open weights models the economies of scale means the cloud beats local significantly and your payback time is like 10 years.
However there are other reasons (e.g. privacy) that might make it worth running locally for some people.
I have just built an Epyc with 512gb DDR4 3200 RAM for a "reasonable" price and I'm hoping to have a setup with GLM as the architect and Qwen 27b/Next Flash as the implementer. This is 1/5 of the price of the Mac, but also probably 1/5 of the speed lol.
I’ll be very curious what you get with DDR4. I also almost went that way. I have an Epyc DDR 5 rig and the best I see is 10 tok/s. Caveat being that’s at Q8 and a 4090 doing pre fill so it could be pushed up.
The surprising thing for me is how much work you will need to cool the banks if you’re near your memory ceiling. My memory starts soft throttling at about 74C (dies may be hotter, that’s the bank temp) and will turn down speed to try to stay below 80.
Happy to send my llama.cpp config settings if you want it.
Curious about that price, if you don't mind sharing a ballpark
Depending on which Epyc you got it might be slower than 1/5 of the speed.
Honestly I suspect neither of them will be performing terribly well but with DDR4 3200 RAM I wonder if you'll be counting tokens per second or seconds per token. I mean, you do at least get a lot of memory channels at least, compared to consumer PCs. I am curious to hear what performance you get, I feel there is not enough information out there on what different setups manage to eek out.
The fastest I was able to get my Threadripper 3960X + 2x 3090s + 256GB DDR4-3200 to run a 2-bit quant of GLM-5.2 was 8 TPS. I would expect to be in seconds-per-token territory for a pure-CPU 4-bit quant.
One thing I'd like to try is MoE offloading: I have 2x32 GiB of VRAM and 128 GiB of DDR5 running at 4800 MT/s (only 2 channels though). I've seen people post difficult to believe MoE offloading results albeit a decently long time ago with older models. Maybe there is a quant that would fit with MoE offloading?
That said, I am guessing my problem is not enough RAM - but this poor consumer platform struggles to do memory training with 128 GiB as it is.
Now I surely regret not having gotten Threadripper and 256 GiB of RAM in the before-times.
My measurement was with MoE offloading, but there's only so much you can keep on-GPU with a 200GB quant and 48GB of VRAM. It's hard to overcome the CPU/RAM bottleneck.
For what it's worth, all of my hardware was used; I think, all-in, I'm probably at around 3k-4k USD? Not cheap, but also not the worst for something relatively versatile.
One could also run it locally on a used dual xeon (or amd-equivalent) server with 512GB RAM, albeit slower, if you have a useful workflow for it that's like "take this day's efforts and run it through various analysis agents", combined with giving it one-shot tasks/modules to build overnight. You would want a place like a garage or basement to put the server because it'll be loud.
> "dual xeon"
Does inference make full use of the memory bandwidth in a NUMA system?
Memory throughput performance is better than many equivalently priced (talking about the cheaper end of the used/refurb market here) single socket systems because each CPU is talking to its own banks of RAM.
Using an example of llama-server on a 256GB (each CPU is attached to 128GB) system running qwen 3.8-flash-next Q8, which takes up about 188GB with the model and context, it's not that the inference engine 'knows' about NUMA, but if you use --load-mode mlock to load the whole thing into memory at once at the time llama-server launches, the kernel will distribute it relatively evenly across the memory connected to both CPUs.
You'd also likely spend far more in electricity than the API cost of processing the prompt(s)
yes, though for some uses, not sending data anywhere to third parties has its own value which is harder to measure.
Well if you did get the m5 ultra could you obliterate the guardrails and then your wife can ask it pertinent but unsafe questions about how to punish you. Seems doable.
"My wife would skin me"
Oh please, as though significant numbers of tech workers on this website are not buying LV, Gucci, and yes Hermes for their pampered wives.
My wife can have an opinion on my tech purchases when I get an opinion on her 20,000$ bag addiction.
And the worst part is both of you should unironically accept these purchases, as somehow luxury bags, high end hardware, AND rolex's often out-pace inflation and are objectively good stores of value in a world of rapidly increasing income/wealth inequality.
Not every tech worker is making top-tier US salaries. For some (I suspect not few) people on HN that $20,000 Mac is almost a year's salary.
You are allowed to have an opinion on her 20k bag addiction
I'd like to ask Sam Altman if he still thinks that it's too dangerous to publish GPT-3. I mean, no one would use it, but what is his reasoning for not publishing it now, in 2026?
I think it would be an important historical document as well. We are potentially looking at the dawn of AGI and one of the most important models ever created. Each model is also a kind of ultimate time capsule, containing a snapshot of the entire human collective mind. If you wanted to ask a 2002 person what they thought about future historical events you can just ask them directly.
There’s not such a straightforward relationship between safety and model sis.
According to the book The Thinking Game, lower quality models at that time were considered less safe, because they could be easily tricked into doing harmful stuff. In the book, Dario (of Anthropic) was the head of safety at openAI and was responsible for pushing for 10x scaling in training to make the models safer.
It does make sense, a smart model is going to be way harder to jailbreak into telling me how to synthesize anthrax (or something).
There are risks associated with releasing historical proprietary models that were not designed for open release:
- It is trivial to extract samples of the training data that was used, which can bolster existing lawsuits/foster new ones.
- Older models are not as safety-hardened, so it is easier to coax unsafe behaviour out of them, which is a PR risk.
- It may be possible to divulge proprietary secrets from the model (e.g. architectural details that may still be relevant).
For these reasons, and more, it's unlikely that GPT-3/similar models will be released until these concerns are no longer relevant (e.g. when they become a purely historic concern, similar to the open-sourcing of other proprietary software from decades ago).
They already publish gpt-oss which is several generations better than gpt-3
GPT-3 is a different model than gpt-oss and is therefore not an answer to the question.
I cannot stand using gpt-oss, but I miss some of the creative spark of GPT-3 davinci dearly.
At release, GPT-OSS was arguably a few generations behind the open frontier.
No? They were the frontier, or near it, at the time of release: https://artificialanalysis.ai/models/releases/gpt-oss-120b
>but what is his reasoning for not publishing it now, in 2026?
What's the point of publishing it when it'll likely be outclassed by gpt-oss?
Maybe I'm reading too much between the lines, but I suspect the reason is to rub his nose in the duplicity or naivety depending on how generous you're feeling. Publishing the model would be a confession that he was wrong.
AI policy is being shaped somewhat by the things Sam and Dario say. So even if you're not feeling vindictive, it's probably good to keep a track record of the previous things they have said as a Bayesian prior. People who don't know better listen to these people, and maybe they shouldn't.
If you want to dunk on sam for "it's too dangerous to publish GPT-3", you hardly need the release of gpt-3 to prove your point. All the other open model releases already provide pretty good evidence. Moreover the fact that the model wasn't release hardly points to the fact that he wanted to save face or whatever. Most AI labs don't release their old proprietary models, so the fact that gpt-3 wasn't released tells us very little.
Thank you, I couldn't have said that in a better way.
I've been using it more and more. Feels like Opus 4.8, in the best possible way.
I'm starting to think Opus 4.8 is significantly smaller than most people assume.
If it's significantly larger than GLM 5.3 (I've heard some insane guesstimates out there like upwards of 5T params or more), that would prove rather embarrassing for Anthropic.
I hear the argument here, but isn't it possible it has dramatically more knowledge and when you get outside the common cases many of us use it for, it'll have completely different capabilities?
I feel like most benchmarks cluster on a reasonably limited area of human knowledge
Sort of depends on how well the core reasoning works. It’s not a big effort to connect an LLM to a search provider.
You do pay for the tokens, but in theory on a smaller model each token is cheaper.
It seems like there is tradeoff between model size and the need for tool use, which - in my mind - is quite costly in terms of time and tokens. More detailed world knowledge requires an exponential increase in model size, but most knowledge can be acquired ad hoc using search or database queries. This will fail for questions where the model lacks the knowledge to ask the right questions, but maybe this could be solved by a handful small inquiry models with different knowledge encoded in their weights?
Or calling into a full-knowledge model “I’m facing problem x, how do I ask myself the right questions?”
I should do that myself, come think of it.
You can't compare models released 6+ months apart. GLM 5.2 was same architecture as 5.3 and not nearly as good. Takes time to build frontier intelligence and distill down to smaller sizes.
It's not that GLM5.3 in full precision unquantized is any smaller, it's 141 * 5.4GB files at approx 770GB which is about the same size as 5.2.
Hold on.. the routed experts are in FP8 now? Previously they were in BF16. Nice, this shall cut my download time by half!
> that would prove rather embarrassing for Anthropic
Not really, in that you just work with different constraints.
Anthropic and US labs in general has maybe 100s to 1000s of GPUs per person to experiment. Zai and Chinese labs in general have 1-10.
The priorities are different.
Do you use it to write HTML/CSS? Javascript? C++? There's a huge difference in ways people use models and if you are not specific about it then your comment means nothing, unfortunately.
I really like how it doesn't have that Claude talk. It just does the thing without Claude's "load-bearing honesty." It's probably my favorite model to interact with, even if it isn't the best or most reliable.
My second favorite model by now is GLM 5.3 flash which is very capable of day to day task. I use it as the main model and GLM 5.3 for task that is more complex
Can you give ant more details how you are you using it? Which harness / service / what you’re building with it etc?
z.ai coder plan, both in opencode and direct API access. I use it for my side projects like https://github.com/scosman/Biscotti (on-device meeting transcription and summaries).
> in the best possible way
You implying its better than opus 5?
I have been only using GLM models since last December and have had the best experience without any drama about tokens and geopolitical restrictions. The quality has been great and I am doing more and more with the latest 5.3 and am really excited that consumer hardware will develop in the next few years where I can run these at home.
h/t to DeepInfra for being the first 3rd party provider for it on OpenRouter (https://openrouter.ai/z-ai/glm-5.3?endpoint=b711bea7-3994-49...).
Have you guys been having a good experience with OpenRouter? I tried it out recently with Claude, and it cached no tokens, charging me $200 for one conversation of 11 messages.
on my TrustedRouter:
z-ai/glm-5.3: also Z.ai, Novita, Atlas Cloud, IO.NET
It's also now live on Ollama Cloud as of a couple minutes ago
I previously posted that DS4Flash was _good_ but not _great_ on two DGX Sparks, but I have to say that GLM-5.3 is pretty amazing. It's been able to tackle all the random hard problems I've thrown at it and it has the intuition that DS4Flash seems to lack.
We're nowhere near a Fable-class model IMO, but things are going to get interesting in this next year.
What quant are you running and tps?
how feasible is it build a SOTA specialized model for some use case e.g. deal sourcing by using this as pre-trained model or a LORA or similar pattern on top? Gonna shoot my shot at a billion dollar business
A stake through Amodei's heart.
Is it possible to fine tune this model and unlock / extend its cybersecurity capabilities? I'm scared that maybe we are not ready for an open-weight model with high cybersecurity skills.
brace yourself
Seeing the price, I am probably just going to keep using GLM-5.2 until 5.3 gets cheaper.
isn't GLM 5.3 flash better than GLM 5.2 overall?
GLM-5.3-Flash is actually cheaper than deepseek and better than deepseek but no one is talking about yet :)
Not in my experience. Tasks that would normally cost $0.08 on DSV4-Flash have cost me $0.30+ on GLM-5.3-Flash. These costs are after Deepseek's recent increase. Also GLM-5.3-Flash is so slow compared to DSV4-Flash. I would be fine with GLM-5.3-Flash if it was cheaper and at the same speed as DSV4.
I use DSV4-Flash on Max through Deepseek's API. I have been using GLM-5.3-Flash on High through Openrouter which I thought had a 50% discount. I must be doing something wrong for the costs to be off this much.
It's actually slightly more expensive ($0.50 vs $0.48), but there's a temporary 50% discount.
I've seen dozens of conversations about it in last 24 hours, and every major inference provided added in first 24 hours. I think it's gaining plenty of traction.
It's interesting that OpenCode Go is treating it as 2x more expensive than DeepSeek Flash, even factoring in the 50% discount
OpenCode Go is probably using quantized down DS4Flash. They outsourced to 3th party providers to keep the cost down, and being able to provide that $30 value (instead of the initial $60 > $15).
We saw the same issue with GLM 5.2 when they still published publicly who the providers are on their website. Most ran FP8 but one was doing FP4, so you had this issue where one moment you had the better FP8 and another session you had the FP4 provider.
You can check the internet archive, it was in the FAQ part before they hide/removed it. So if you looked up the providers, and the published quants, yea, ...
Given that a lot of complaints are coming from people that felt OpenCode Go Flash feel like a step down compared to old OpenCode Go/DeepSeek API directly, it smells of a quantized down provider is mixed in.
OpenCode Go is becoming less of a good deal by the month. I pretty much only use it for mimo 2.5 pro now, and everything else is either ollama or openrouter.
Go has API pricing + this weird scaling of how much is it worth. Some models get $60 of usage, some $30 and some $15 etc.
I've been using it quite a bit too. My main complaint is that it can be really slow sometimes — like, really slow — and the speed feels pretty inconsistent.
z.ai is using all Chinese hardware for flash: https://thenewstack.io/glm-5-3-flash-chinese-chips/
There are other providers with much faster inference, like BaseTen at >100t/s: https://openrouter.ai/z-ai/glm-5.3-flash#performance
How do I find out where the openrouter model providers' servers are located?
If you click on the provider name, the panel that pops up shows a "Region" value. Not every provider lists their region, however.
I think the region is just the HQ of the provider. So z.ai's region is Singapore but it's quite likely that their servers are actually in China
It’s cheaper sure, but it’s very slow. It’s not a drop in replacement
I think we don't have a good draft model for better speculative decoding yet (e.g. DFlash 2). Once we do, it will be faster.
It very well could be faster, but right now it isn’t.
It is a slow for me through z.ai; it does not feel 'flash' at all. But then neither did the new DS Flash. I think they were getting hammered.
glm 5.3 flash is the best model I have ever used
How much usage do you find you get on these kinda models (I know the pricing changes a bit) compared to a $20 sub say for Google AI Pro in anti gravity?
I hate how difficult it is to compare prices when looking at subscriptions.
Would $20 in open router, using models like GLM get me more or less?
I think it'd get you less than a $20 sub to any of the big three. I've used it on OpenRouter and found it kind of expensive for the results, but that might change now that it's open weight and other providers can host it/compete with Z.ai. For the work I did with it, I would've rather used DeepSeek V4 Flash just because it's more economical and still gives good results IMO.
Z.ai does have their own subscription, but I haven't used it because their privacy policy was pretty buns last time I checked.
just tested (zai-org/GLM-5.3-Flash via together.ai) against latest DeepSeek-V4-Flash for a very specific task and thought i'd report here...
- price: DS4 wins... $0.0235 vs $0.0242 for ten tasks
- latency: GLM wins... 108s total against 154s
this is for a personal use-case where i'm detecting ads in a written transcript. sticking with ds4-flash for now since latency is not a critical factor