371–380 of 476 posts
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distillation of thinking models is not particularly effective - both "Open"AI and Misanthropic don't show you the real chain of thought, only its severely downscaled version. both do everything in their power to combat such outrageous copyright infringement, so the bulk of unethically scrapped data the Chinese have is from several generations ago.
I don’t understand why there isn’t public dataset for reasoning that can be improved by humans/llms like Wikipedia (ie with auto judging contributions etc).
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It's a surprisingly common misconception that models contain any metadata at all about themselves in their weights. If you ask them, "What model are you?" they either retrieve the answer from the system prompt, or they hallucinate an answer. Same goes for questions about knowledge cut-off, how many parameters they have, the source of their training data, etc.
Huh. That kinda makes sense. So you think it's hallucinating it's model name?
The requirements to run this model locally: https://www.reddit.com/r/LocalLLaMA/comments/1u8ai2a/glm52_i...
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Configure a subagent in your coding harness to spin up a new sub-session with any vision model for those tasks and feed the result back to the main model. No need for "one model that does everything"
That doesn’t work well in a lot of scenarios. The text LLM doesn’t know what to look for in an image before it sees a description, you might need multiple rounds of back and forth.
I have a script that ranks these based on codingindex from Artificial Analysis. All it does is pull a json from their main table page and parses it with the fields I care about (coding). There used to be a mailing list associated with it but eh ... there wasn't much interest. I use the script every day though. Current partial output score age size name 47.1 58 large Kimi K2.6 47.5 54 large DeepSeek V4 Pro (Reasoning,…
It seems like it's up for the task of complex code, but those little paper-cuts are scary to me. I wouldn't trust this model for anything remotely serious.
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GLM 5.2 Max = Opus 4.8 Max in thinking behavior. The thinking chain is so similar, and so is the amount of token usage on the output. If you want reasonable token usage, you need to run it GLM 5.2 at High. There is little drop in quality from Max to High (for most tasks). And it cuts token usage by 2 a 2.5x. GLM 5.2, Max is really something you only need for complex tasks. In essence, GLM 5.2 is Opus 4.8 its little b…
distillation of thinking models is not particularly effective - both "Open"AI and Misanthropic don't show you the real chain of thought, only its severely downscaled version. both do everything in their power to combat such outrageous copyright infringement, so the bulk of unethically scrapped data the Chinese have is from several generations ago.
To be clear, if Anthropic was using totally licensed data, I'd be sympathetic to these claims. But if you're going to pirate the world's creativity you'd better be willing to gimme dat shit for free[0].
[0] As said by Hungry Santa.
> On the Intelligence vs. Cost per Task Pareto Frontier: GLM-5.2 is on the Pareto frontier of the Intelligence vs Cost per Task chart, with the lowest cost per task among models at its intelligence level. GLM-5.2 costs ~$0.46 per task, compared to GLM-5.1 ($0.25), Kimi K2.6 ($0.31), MiniMax-M3 ($0.18) and DeepSeek V4 Pro (max, $0.05) am i missing something?
I added it to my benchmark based on Mythos-reported bugs, and it's better than GLM 5.1, but still behind several other models, maybe most directly comparable to Qwen 3.7 Max. But, several other open models, including small self-hostable ones (Gemma 4 and Qwen 3.6), found the same number of bugs, 3 of 9. Though it also gets partial credit for reporting one bug in the right spot, but kinda misunderstanding the bug. I a…