i think GLM 5.2 is not cheap and not easy to get the coding plan... so even it's on the Opus level... still not attractive.
How is it not easy to get the coding plan?
GLM 5.2 vs. Opus
121–130 of 367 posts
Re: GLM 5.2 vs. Opus
#122Earlier quoted context omitted.
The argument is flawed, there is no logical reason to assume a single prompt won’t be sufficient to constitute the complexity of a software project. It may not be practical in many cases but there is too much variability in what is considered a complex software project and in the sufficiency of instruction in a single prompt to make that claim and say it’s “by definition.”
And that prompt will basically be 2000 page spec Bible à la IBM circa 1960, see waterfall. Unless AI develops mindreading (and advanced mindreading at that), single prompt creation of actual complex software products will never happen. You'll one shot a simple non scientific calculator, but not Excel or Vim or Nginx.
Why not? Given a proper spec, you should absolutely be able to one-shot Excel, particularly if we put it at the level of complexity of, say, Excel 1.0 for Mac.
Current models aren't capable of that, but that doesn't mean it's not possible.
Re: GLM 5.2 vs. Opus
#123You 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!
Re: GLM 5.2 vs. Opus
#124Earlier quoted context omitted.
My impression is that individual subscriptions are the loss leading hook. The money is made on Enterprise token contracts. Employees and students used to coding with thousands of dollars worth of tokens (on a 20/100 dollar plan) will push enterprise to spend. Having a Chinese model that is competitive won't displace this enterprise spend. But an open model hosted in the US/EU might. The existence of GLM 5.2 puts a ce…
> 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.
Re: GLM 5.2 vs. Opus
#125I'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…
Its also nice that you can see its entire reasoning trace. I can see it going off the rails - or see something I forgot to tell it - and stop and correct it. Or I'll learn WHY it made the choice it did and not have to question it after.
Re: GLM 5.2 vs. Opus
#126Earlier quoted context omitted.
My impression is that individual subscriptions are the loss leading hook. The money is made on Enterprise token contracts. Employees and students used to coding with thousands of dollars worth of tokens (on a 20/100 dollar plan) will push enterprise to spend. Having a Chinese model that is competitive won't displace this enterprise spend. But an open model hosted in the US/EU might. The existence of GLM 5.2 puts a ce…
> 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.
Re: GLM 5.2 vs. Opus
#127I'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…
Also pricing, I wanted to give a try, but when pricing is only 30% cheaper than Opus, I wouldn't go for it with these issues.
What?
It is less than 20% of the cost of Opus at API rates. 1.40/4.40 vs 5/25.
Re: GLM 5.2 vs. Opus
#128Pretty clearly it's beating Opus at [web dev]( https://www.gptbased.com/ ) - on price, on score.. I mean what else is there?
I hate to be that guy, but real privacy policy on training data/it being hosted somewhere where I'm not worried about secrets being stored/leaked.
2016 me would agree, but 2026 me looks at Trump and Dario, and at China, sees basically no ethical difference (or possibly even an ethical deficit for America) and considers that perhaps it's better to go with the option that isn't trying to hoodwink me with bullshit platitudes and flag waiving while doing whatever they want in actuality.
Re: GLM 5.2 vs. Opus
#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
Re: GLM 5.2 vs. Opus
#130Earlier quoted context omitted.
And that prompt will basically be 2000 page spec Bible à la IBM circa 1960, see waterfall. Unless AI develops mindreading (and advanced mindreading at that), single prompt creation of actual complex software products will never happen. You'll one shot a simple non scientific calculator, but not Excel or Vim or Nginx.
Why not? Given a proper spec, you should absolutely be able to one-shot Excel, particularly if we put it at the level of complexity of, say, Excel 1.0 for Mac. Current models aren't capable of that, but that doesn't mean it's not possible.
The issue is not the models, the issue is that this method ws tried before, and humans suck at writing what they want. Developing in small increments allowing feedback was an answer to this issue.
If you made models able to code to long spec, you would be left with the hard issue of having to write them.