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GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

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Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#221

Post by InsideOutSanta »

Aurornis wrote: post #181

The benchmarks are impressive, but it's comparing to last generation models (Opus 4.5 and GPT-5.2). The competitor models are new, but they would have easily had enough time to re-run the benchmarks and update the press release by now. Although it doesn't really matter much. All of the open weights models lately come with impressive benchmarks but then don't perform as well as expected in actual use. There's clearly…

> it's comparing to last generation models (Opus 4.5 and GPT-5.2).

If it's anywhere close to those models, I couldn't possibly be happier. Going from GLM-4.7 to something comparable to 4.5 or 5.2 would be an absolutely crazy improvement.

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#222

Post by woeirua »

syntaxing wrote: post #207
woeirua wrote: post #190

It might be impressive on benchmarks, but there's just no way for them to break through the noise from the frontier models. At these prices they're just hemorrhaging money. I can't see a path forward for the smaller companies in this space.

Tim Dettmers had an interesting take on this [1]. Fundamentally, the philosophy is different. >China’s philosophy is different. They believe model capabilities do not matter as much as application. What matters is how you use AI. https://timdettmers.com/2025/12/10/why-agi-will-not-happen/

Sorry, but that's an exceptionally unimpressive article. The crux of his thesis is:

>The main flaw is that this idea treats intelligence as purely abstract and not grounded in physical reality. To improve any system, you need resources. And even if a superintelligence uses these resources more effectively than humans to improve itself, it is still bound by the scaling of improvements I mentioned before — linear improvements need exponential resources. Diminishing returns can be avoided by switching to more independent problems – like adding one-off features to GPUs – but these quickly hit their own diminishing returns.

Literally everyone already knows the problems with scaling compute and data. This is not a deep insight. His assertion that we can't keep scaling GPUs is apparently not being taken seriously by _anyone_ else.

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#223

Post by bigyabai »

jsheard wrote: post #220
bigyabai wrote: post #213

Earlier quoted context omitted.

> That's like ten normal computers worth of power for the GPUs alone. Maybe if your "computer" in question is a smartphone? Remember that the M3 Ultra is a 300w+ chip that won't beat one of those 3090s in compute or raster efficiency.

I wouldn't class the M3 Ultra as a "normal" computer either. That's a big-ass workstation. I was thinking along the lines of a typical Macbook or Mac Mini or Windows laptop, which are fine for 99% of anyone who isn't looking to play games or run gigantic AI models locally.

Those aren't "normal" computers, either. They're iPad chips running in the TDP envelope of a tablet, usually with iPad-level performance to match.

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#224

Post by re-thc »

syntaxing wrote: post #207
woeirua wrote: post #190

It might be impressive on benchmarks, but there's just no way for them to break through the noise from the frontier models. At these prices they're just hemorrhaging money. I can't see a path forward for the smaller companies in this space.

Tim Dettmers had an interesting take on this [1]. Fundamentally, the philosophy is different. >China’s philosophy is different. They believe model capabilities do not matter as much as application. What matters is how you use AI. https://timdettmers.com/2025/12/10/why-agi-will-not-happen/

When you have export restrictions what do you expect them to say?

> They believe model capabilities do not matter as much as application.

Tell me their tone when their hardware can match up.

It doesn't matter because they can't make it matter (yet).

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#227

Post by btown »

simonw wrote: post #215

Pelican generated via OpenRouter: https://gist.github.com/simonw/cc4ca7815ae82562e89a9fdd99f07... Solid bird, not a great bicycle frame.

Thank you for continuing to maintain the only benchmarking system that matters!

Context for the unaware: https://simonwillison.net/tags/pelican-riding-a-bicycle/

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#228

Post by ionelaipatioaei »

Aurornis wrote: post #181

The benchmarks are impressive, but it's comparing to last generation models (Opus 4.5 and GPT-5.2). The competitor models are new, but they would have easily had enough time to re-run the benchmarks and update the press release by now. Although it doesn't really matter much. All of the open weights models lately come with impressive benchmarks but then don't perform as well as expected in actual use. There's clearly…

I think the only advantage that closed models have are the tools around them (claude code and codex). At this point if forced I could totally live with open models only if needed.

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#229

Post by westernzevon »

nullbyte wrote: post #214

GLM 5 beats Kimi on SWE bench and Terminal bench. If it's anywhere near Kimi in price, this looks great. Edit: Input tokens are twice as expensive. That might be a deal breaker.

It seems to be much better at first pass tho. We'll see how real costs stack up

Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks

#230

Post by manofmanysmiles »

pcwelder wrote: post #187

It's live on openrouter now. In my personal benchmark it's bad. So far the benchmark has been a really good indicator of instruction following and agentic behaviour in general. To those who are curious, the benchmark is just the ability of model to follow a custom tool calling format. I ask it to using coding tasks using chat.md [1] + mcps. And so far it's just not able to follow it at all. [1] https://github.com/rus…

I love the idea of chat.md.

I'm developing a personal text editor with vim keybindings and paused work because I couldn't think of a good interface that felt right. This could be it.

I think I'll update my editor to do something like this but with intelligent "collapsing" of extra text to reduce visual noise.

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