[behnamoh]

Times like this remind me that despite GLM and Codex and other models being hyped up as Claude Opus 4.8 replacements, I still would not trust them with my most important work. For example, right now I'm working on a huge refactoring project, and even Opus has struggled with it after several days. I cannot even imagine how GLM, Codex, or other models would handle this. So the only option for me is to wait until this outage is over.

And it's not like open models are cheap to run even as alternatives. For example, with my $100/mo subscription for Claude Code, I often burn more than $100 a day several times a week. But if I were to use the API of GLM, it would be about $300.

[lkois]

Since you cannot imagine how they'd perform, isn't this the perfect opportunity to test your assumption?

[mixtureoftakes]

yeah and lets not forget codex and glm have subscriptions too, with even more usage per dollar

[behnamoh]

but they also burn more tokens per task, so in the end, Claude comes out as the more efficient one, despite giving you less tokens.

[viraptor]

You've got it backwards. Opus is the token/money burning one https://deepswe.datacurve.ai/

Gpt 5.5 uses a third of the opus 4.8 tokens for the same task and scores higher. Glm 5.2 was worse in quality but used half the tokens - 5.3 is not tested yet but will be higher.

[jliptzin]

It depends what you’re doing. I have no problem using an open source model to build a static website or other simple tasks. And of course, open source is catching up and the things you can trust it with grows every month.

Not to mention the way the proprietary models patronize you if they think you’re up to no good. The other day I was trying to use Claude to transcribe a song on YouTube to sheet music; the link was broken and it had not been available for purchase for years. Of course I first had to prove to Claude that I was not just being cheap and trying to get around paying the $5 for the download, which I would have had no problem with. “I am going to be up front with you —- my research raises some red flags with your initial premise that the sheet music is no longer available…”

I think in the next 5-10 years inference costs will come down substantially and the open source models will get so advanced that only someone in an extremely niche, cutting edge field will need a frontier model. Everyone else will have a dedicated box somewhere on their network that runs their LLM of choice. No tokens, none of your data getting sent to a third party, no arguing with it over whether or not a link is actually broken or if you’re just trying to be cheap.

I think OpenAI and Anthropic realize this which is the reason they’re in a rush to go public.

[trollbridge]

z.ai has subscription based plans if you want to use them to use GLM. (Same for Codex, although I'm not going to pretend GPT-5.5 is as strong of a model as Opus.)

I would give GLM a try. I'm shocked at how well it's been able to handle some things I've thrown at it.

[Der_Einzige]

A lot of the perception of open source models being garbage is that they're still using the same piss-poor sampling algorithms that OpenAI/Anthropic force on their users, i.e. Top-p, top-k.

These lead to small accumulation of sampling errors which makes it all but inevitable that open source models will shit the bed by the 200K token mark or even sooner.

If you set your opencode to use a good sampling algorithm, such as min_p or top-n sigma (llamacpp supports both), you'll find that at least for long running tasks, your model gets a lot better.

It won't make GLM as good as Opus 4.8, but it will stop the feeling of "brain damage" from running open source models at the edge of their context windows.

And yes, there is an upcoming (hopefully NeurIPS) paper titled "Long Context Generation is a Sampling Problem" for more details about this. Give it two months and it'll be on Arxiv one way or another.