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- Hacker News
- > 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?
by tensegrist - Knowing very little about how to run these, how close are we to medium or larger businesses starting to buy hardware to run models like this to keep the models local?
It’s expensive, and not as capable as the frontier models, but would have some pretty big benefits around privacy and agency.
by CubsFan1060 - I've been playing with this model a fair amount over the last 24 hours, and I can confirm it's quite capable, while being a little bit verbose (I've seen it reconsider things 3-4 times in thinking traces before deciding on a path forward), and not being quite as good as GPT5.5 at working through complex abstract requirements.
Honestly it's good enough that I feel comfortable recommending a Z.AI sub + a $20/mo OpenAI sub for all but the most AI pilled multi-orchestrators, or the die hard Claude fans. GLM writing + GPT reviewing/debugging feels pretty unlimited and minimally worse than just doing everything in GPT with the $200/mo plan.
by CuriouslyC - Artificial Analysis coding benchmark shows GLM5.1 on high pretty close to GPT5.5 xhigh in cost to run, with GPT5.5 on medium significantly less expensive. Compared to GPT5.5 medium GLM5.1xhigh is twice the cost and half the intelligence. They don't have GLM5.2 on there yet, but that'd a big gap to bridge.
https://artificialanalysis.ai/agents/coding-agents?coding-ag...
I thought I was "holding it wrong" until DeepSWE came along -- personally it seems to match my own experiences pretty well. Really makes me wonder how legitimate some of the internet noise is about open models. There's surely some use cases for them, not everything needs the absolute frontier (GPT5.5 on low is awesome), but if you want to be near the frontier everyone needs to be honest about the fact that we're only talking about Opus, Fable, GPT5.5.
by mrngld - I was surprised that GLM 5.1/5.2 are not vision models - they are text input only.
That's actually pretty uncommon these days. All of the OpenAI/Anthropic/Gemini models accept images, and so do the other leading open weight families - Gemma 4, Qwen 3.6, Kimi 2.x.
In GLM's case image input would be useful because it's a model that scores very highly for tasks like web design, but without image input it can't take a screenshot and output HTML+CSS.
Don't get me wrong, GLM is a phenomenal model, but the image thing is a bit of a gap.
by simonw - Why aren't more people talking about this? It's literally Opus 4.7 quality stupid prices. I know providers who are offering this at unlimited tokens for $50 a month. Some are even offering API rates at 3x lower than the official ZAI api rates which are already like 10x cheaper than Opus. (Crof and Umans btw)
This is a huge blow to Anthropic/OpenAI/Google and a massive win for the rest of the world. The official API prices and speeds mean nothing for open source models.
by unrvl22 - I have a script that ranks these based on codingindex from Artificial Analysis.
All it does is pull a json from their main table page and parses it with the fields I care about (coding).
There used to be a mailing list associated with it but eh ... there wasn't much interest. I use the script every day though.
Current partial output
To see everything, run it like soscore age size name 47.1 58 large Kimi K2.6 47.5 54 large DeepSeek V4 Pro (Reasoning, Max Effort) 47.5 70 - Muse Spark 47.6 132 - Claude Opus 4.6 (Non-reasoning, High Effort) 47.8 205 - Claude Opus 4.5 (Reasoning) 48.1 132 - Claude Opus 4.6 (Adaptive Reasoning, Max Effort) 48.6 55 - GPT-5.5 (Non-reasoning) 48.7 188 - GPT-5.2 (xhigh) 50.1 29 - Qwen3.7 Max 50.7 1 large GLM-5.2 (max) 50.9 120 - Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) 51.5 92 - GPT-5.4 mini (xhigh) 52.1 55 - GPT-5.5 (low) 52.5 62 - Claude Opus 4.7 (Adaptive Reasoning, Max Effort) 53.1 132 - GPT-5.3 Codex (xhigh) 53.1 62 - Claude Opus 4.7 (Non-reasoning, High Effort) 55.5 118 - Gemini 3.1 Pro Preview 56.2 55 - GPT-5.5 (medium) 56.7 20 - Claude Opus 4.8 (Adaptive Reasoning, Max Effort) 57.2 104 - GPT-5.4 (xhigh) 58.5 55 - GPT-5.5 (high) 59.1 55 - GPT-5.5 (xhigh) 62 8 - Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback)
The repo: https://github.com/day50-dev/aa-eval-email$ curl day50.dev/art-analysis.sh | bashsome key takeaways:
* open models are on about a 4-7 month lag right now depending on how you want to measure it
* if this keeps up, you might see an open-weights model doing claude fable 5 level work before the new year.
if people sign up for the free mailing list (that just does this) I'll go and put it back on ... emails when new model evals drop - it was pretty useful.
by kristopolous - It seems to really be a nice step-up and is getting quite close to the frontier. I wish they'd start focusing on the reasoning efficiency now, though. I have a simple (relatively) test task to evaluate LLMs: writing a simple math evaluator library in Nim (it's about 400-600 lines total max), and GLM 5.2 (xhigh which maps to max effort) spent over 15 minutes (!) reasoning, spending about 45k tokens, before it finally wrote the first file.
I know it's hard to improve on that, but now that their models are good enough at raw intelligence, I think this should become a higher priority task.
Currently on https://artificialanalysis.ai/#output-tokens GPT 5.5 xhigh spends 16k tokens total on average, GPT 5.5 high is 10k, Fable 5 33k, Opus 4.8 41k, GLM 5.2 is 42k. GPT 5.5 is extremely reasoning efficient.
Of course if you convert those values to actual request cost, GLM 5.2 will probably beat GPT 5.5/Opus 4.8, but speed matters for a lot of people, I think.
by Tiberium