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GLM 5.2 vs. Opus

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Re: GLM 5.2 vs. Opus

#131

Post by CuriouslyC »

em500 wrote: post #43
jkwang wrote: post #7

GLM-5.2 is quietly becoming the most interesting open model release this year. The coding benchmarks are surprisingly close to frontier models at a fraction of the inference cost.

We've had the great small Qwen 3.6 early April that many could actually run on their laptop. Then similar from Google a few weeks later (Gemma4, better in prose, worse in code). Then the super cheap large Deepseek V4 a few weeks later. Then antirez DS4 build that made that actually runnable on MacBooks and Mac Studios. And now the "near-frontier / near-Opus" GLM 5.2. For people who follow open LLMs, none of these wer…

The difference here is that those small models are impressive, but not super useful. Deepseek 4 is impressively cheap for the intelligence, but not reliable enough to daily drive unless your time has low value.

GLM passes a meaningful threshold of reliability/utility that puts it in a different category for real work. Just like Opus really took off after passing a threshold with 4.5. It's the first open model to do that.

Re: GLM 5.2 vs. Opus

#132

Post by trollbridge »

xlii wrote: post #6

I've been checking out GLM 5.2 on some projects and few thoughts on it: - it takes it sweet time to get code rolling, not the fastest model by any means - it strays a lot during discovery/planning but then corrects - it's not steering friendly, as it hallucinates things that it doesn't follow later on - its output is quite good A sample use case: I was optimizing rendering on Swift+Zig codebase. It chocked on 5k data…

I used it the other day for something of low importance that other models simply weren't figuring out and I didn't want to burn up Opus 4.8 on. (It had to do with overriding left-click on a macOS menu bar and then making Ctrl+click or right click bring up the menu like left-click normally does, and doing all this conditionally.)

Switched the model to GLM-5.2 halfway in the middle of a troubleshooting session (didn't even bother to reprompt, just changed it in the middle of its reasoning), gave it a few minutes, problem fixed. This is with the subscription based allocation on OpenCode Go, where a problem like this would completely burn up my Opus for the current 5 hours or even the current week.

Re: GLM 5.2 vs. Opus

#133

Post by CuriouslyC »

KronisLV wrote: post #62
greyman wrote: post #5

>On output tokens, GLM-5.2 is less than a fifth the price of Opus. Opus is most expensive model in pay as you go model, but IMO fair comparison should include subscription price as well. For example when one has $100 Claude Max and use it up through the month, it might not be more expensive than GLM, or at least not 5x.

> For example when one has $100 Claude Max and use it up through the month, it might not be more expensive than GLM, or at least not 5x. https://z.ai/subscribe I’m currently trying to figure out whether a downgrade from Max 5x to Pro in combination with one of those would save me money and if so, how much. Edit: seems like Anthropic Pro + GLM Pro (Yearly) would let me almost halve my costs of Anthropic Max 5x. Only c…

I'm considering shifting to the OpenAI $20 plan + GLM. OAI has the best computer use, vision support and the best programming intelligence of any model short of Mythos/Fable, and the quota is a lot more generous than the Anthropic $20 plan.

Re: GLM 5.2 vs. Opus

#134

Post by Babooz »

jameswhitford wrote: post #123
CuriouslyC wrote: post #119

You should repeat this experiment but with progressively more detail in the initial prompt. Claude's secret sauce is taking weakly specified prompts and making passable things from them, but as the degrees of freedom in the prompt go down Claude starts to disobey while other models close in on the intent.

That is a great suggestion that I am definitely going to look into, thanks!

Nice comparison, but perhaps a more informative one would be to keep the harness the same and use Claude Code for both model. In your comparison, the differences could be due to many harness design decisions.

Re: GLM 5.2 vs. Opus

#135

Post by twobitshifter »

js4ever wrote: post #129

"GLM-5.2 hit a problem here, because it can't read images. It isn't multimodal. So instead of looking at a screenshot, it fell back on a hacky workaround: it wrote scripts to read the raw pixel data and check whether the colors came out roughly as expected." A better way would be to use https://github.com/openbmb/MiniCPM-V

Right, just give the text llm access to a vision specific agent and that problem can be solved. Or if you really want let it even call Opus with an image - seems like you’d still save money

Re: GLM 5.2 vs. Opus

#136

Post by trollbridge »

ulrikrasmussen wrote: post #30

> Through an API it costs a fraction of Opus, and you can run it yourself for free if you have the hardware. I haven't been keeping up on hardware costs for state of the art LLM inference, but this remark made me ask myself how many readers of the article would actually be able to run this model on hardware they own. How much would it cost to acquire such a setup?

GLM-5.2 performing like it would from a good provider - 8x B200s, so $450k. (No personal experience here)

GLM-5.2, severely quantised, 512GB Mac Studio, somewhere between $10k-$35k for a used M3. Or run it on a CPU with 768GB of RAM by getting an old PowerEdge with DDR4 for around $5,000.

Qwen-3.6-35b-q6, runs well on an RTX 5090 ($4000 + cost of a PC), runs medicore on an Intel Arc B70 ($1000 + cost of a PC plus lots of fiddling to get the setup to work right).

Gemma is a good candidate for the cheaper stuff, but I lack personal experience with using it locally

Re: GLM 5.2 vs. Opus

#137

Post by LUmBULtERA »

Certhas wrote: post #124
LUmBULtERA wrote: post #118

Earlier quoted context omitted.

> My impression is that individual subscriptions are the loss leading hook Except there is no evidence of this at all, just people comparing API and subscription pricing. The leaked financial info for OpenAI shows inference is profitable right now, though it does not show a distinction between subscription and API revenue... but if subscription revenue was so lossy, it would hard for total inference to still be profi…

I did clearly say "my impression is". And you have no evidence to the contrary. We don't even reliably in w how many subscribers Vs enterprise customers they have. And the OpenAI leak doesn't even cleanly say that inference is profitable from what I can tell... The better evidence that it probably is are the prices charged by open weight model providers.

Fair enough, there is not strong specific evidence to the contrary except about overall inference being profitable for OpenAI (as well as the open weight model providers hosted throughout the world).

Re: GLM 5.2 vs. Opus

#138

Post by LUmBULtERA »

CuriouslyC wrote: post #126
LUmBULtERA wrote: post #118

Earlier quoted context omitted.

> My impression is that individual subscriptions are the loss leading hook Except there is no evidence of this at all, just people comparing API and subscription pricing. The leaked financial info for OpenAI shows inference is profitable right now, though it does not show a distinction between subscription and API revenue... but if subscription revenue was so lossy, it would hard for total inference to still be profi…

Anthropic has indicated in the past that API gross margins are ~60%. This might have improved since then, though competition from OAI puts a ceiling on that.

Subscription inference can also be cheaper than the cost of API inference if the provider wants it to -- providers can do flexible scheduling for subscription inference for example, around API inference, to lower its cost and get better utilization of the hardware.

Re: GLM 5.2 vs. Opus

#139

Post by trollbridge »

cicko wrote: post #54
jack_pp wrote: post #33

Earlier quoted context omitted.

This framing local LLMs as free is stupid. Basically pay 100+ months worth of API costs up front isn't free in the slightest. And it will be slower than non-local, your hardware will be outdated in 12 months and probably won't be able to run SOTA at anywhere near non-local speed in max 20 months

True. But there are other meanings of "free". I.e. nobody can say "from now on you no longer have access to model X because you're an asshole"

Some obvious examples of why you'd want to spend the capital on this would be, for example, making some kind of autonomous system which needs to be periodically be offline, or you need complete confidentiality of what you're using the model for, etc.

To be cost effective with inference providers, you have to find some way to be using it 24/7.

Re: GLM 5.2 vs. Opus

#140

Post by twobitshifter »

xg15 wrote: post #69

So GLM emits fewer tokens and does fewer tool calls, but still takes over twice as long to complete. Can someone explain to me where that time usage is coming from if not from the model operation itself? Are the individual tool calls more complex and take more time to complete? Or is the rate of tok/s lower because the model does more compute per token?

Probably the data center where the model is running more than anything. Another option is if Opus is using anything like a Mixture of Experts approach, in which case the amount of the model loaded in memory at one time could be smaller than GLM.
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