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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

#311

Post by GorbachevyChase »

mark_l_watson wrote: post #291
ionelaipatioaei wrote: post #231

Earlier quoted context omitted.

I honestly feel like people are brainwashed by anthropic propaganda when it comes to claude, I think codex is just way better and kimi 2.5 (and I think glm 5 now) are perfectly fine for a claude replacement.

So much money is on the line for US super scalers that they probably pay for ‘pushes’ on social media. Maybe Chinese companies are doing the same.

I would say that’s more certain than just a “probably“. I would bet that some of the ridiculous fear mongering about language models trying to escape their server, blackmail their developers, or spontaneously participating in a social network are all clandestine marketing campaigns. The technology is certainly amazing and very useful, but I don’t think any of these terminator stories were boosted by the algorithms on their own.

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

#312

Post by 2001zhaozhao »

GLM-4.7-Flash was the first local coding model that I felt was intelligent enough to be useful. It feels something like Claude 4.5 Haiku at a parameter size where other coding models are still getting into loops and making bewilderingly stupid tool calls. It also has very clear reasoning traces that feel like Claude, which does result in the ability to inspect its reasoning to figure out why it made certain decisions.

So far I haven't managed to get comparably good results out of any other local model including Devstral 2 Small and the more recent Qwen-Coder-Next.

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

#313

Post by Balinares »

Aurornis wrote: post #253
dust42 wrote: post #233

Earlier quoted context omitted.

They are all just token generators without any intelligence. There is so little difference nowadays that I think in a blind test nobody will be able to differentiate the models - whether open source or closed source. Today's meme was this question: "The car wash is only 50 meters from my house. I want to get my car washed, should I drive there or walk?" Here is Claude's answer just right now: "Walk! At only 50 meters…

If you're asking simple riddles, you shouldn't be paying for SOTA frontier models with long context. This is a silly test for the big coding models. This is like saying "all calculators are the same, nobody needs a TI-89!" and then adding 1+2 on a pocket calculator to prove your point.

I find it's a great test, actually. There are lots of "should I take the car" decisions in putting together software that's supposed to do things, and with poor judgement in how the things should be done, you typically end up with the software equivalent of a Rube-Goldberg machine that harnesses elephants to your car and uses mice to scare the elephants toward the car wash while you walk. After all, it's a short distance, isn't it?

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

#314

Post by khimaros »

2001zhaozhao wrote: post #312

GLM-4.7-Flash was the first local coding model that I felt was intelligent enough to be useful. It feels something like Claude 4.5 Haiku at a parameter size where other coding models are still getting into loops and making bewilderingly stupid tool calls. It also has very clear reasoning traces that feel like Claude, which does result in the ability to inspect its reasoning to figure out why it made certain decisions…

minimax-m.2 is close

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

#315

Post by solarized »

simonw wrote: post #215

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

This Pelican benchmark has become irrelevant. SVG is already ubiquitous.

We need a new, authentic scenario.

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

#316

Post by miki123211 »

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…

Anthropic, OpenAI and Google have real user data that they can use to influence their models. Chinese labs have benchmarks. Once you realize this, it's obvious why this is the case.

You can have self-hosted models. You can have models that improve based on your needs. You can't have both.

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

#317

Post by qiine »

miroljub wrote: post #304
Scene_Cast2 wrote: post #244

Earlier quoted context omitted.

I just ran this with Gemini 3 Pro, Opus 4.6, and Grok 4 (the models I personally find the smartest for my work). All three answered correctly.

They had plenty of time to update their system prompts so they don't be embarrassed. I noticed whenever such meme comes out, if you check immediately you can reproduce it yourself, but after a free hours it's already updated.

The road to AGI is weirder than anticipated

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

#319

Post by Gracana »

mythz wrote: post #113
oceanplexian wrote: post #99

Earlier quoted context omitted.

Doing inference with a Mac Mini to save money is more or less holding it wrong. Of course if you buy some overpriced Apple hardware it’s going to take years to break even. Buy a couple real GPUs and do tensor parallelism and concurrent batch requests with vllm and it becomes extremely cost competitive to run your own hardware.

> Doing inference with a Mac Mini to save money is more or less holding it wrong. No one's running these large models on a Mac Mini. > Of course if you buy some overpriced Apple hardware it’s going to take years to break even. Great, where can I find cheaper hardware that can run GLM 5's 745B or Kimi K2.5 1T models? Currently it requires 2x M3 Ultras (1TB VRAM) to run Kimi K2.5 at 24 tok/s [1] What are the better val…

Six months ago I'd have said EPYC Turin. You could do a heck of a build with 12Ch DDR5-6400 and a GPU or two for the dense model parts. 20k would have been a huge budget for a homelab CPU/GPU inference rig at the time. Now 20k won't buy you the memory.
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