Live data from Hacker News

GLM 5.2 beats Claude in our benchmarks

semgrep.dev

231–240 of 559 posts

Re: GLM 5.2 beats Claude in our benchmarks

#231

Earlier quoted context omitted.

Why Deepseek v4 flash is better than pro in your benchmarks?

I have also found deepseek flash beat pro in some of my own internal evals for tasklet.ai it’s really surprising and I don’t understand it

The rumour is that it's trained on Opus, but who knows

Re: GLM 5.2 beats Claude in our benchmarks

#232

Earlier quoted context omitted.

GLM 5.2 is a great model, but if you only want to use the best model available, it isn't there yet. Every lab releases models that memorize benchmark answers, both intentionally and unintentionally. But we consistently find that models from Chinese labs have a wider gap between public benchmarks and our evaluations, which we designed to be less vulnerable to benchmaxxing. In multi-agent coding environments, GLM 5.2 i…

Man, there is exactly zero information on your site about how your benchmarks work. Why should one trust your numbers when there is no way to verify them?

Scroll to the bottom for the methodology (sorry, this should be linkable)

Re: GLM 5.2 beats Claude in our benchmarks

#233

Earlier quoted context omitted.

GLM 5.2 is a great model, but if you only want to use the best model available, it isn't there yet. Every lab releases models that memorize benchmark answers, both intentionally and unintentionally. But we consistently find that models from Chinese labs have a wider gap between public benchmarks and our evaluations, which we designed to be less vulnerable to benchmaxxing. In multi-agent coding environments, GLM 5.2 i…

Why Deepseek v4 flash is better than pro in your benchmarks?

It's 100% due to tool use -- Flash adapts much better to our custom harness with tool names that are not identical to what models were likely trained on. DeepSeek V4 Pro performs much worse in that aspect than almost all other recent releases, for whatever reason.

Re: GLM 5.2 beats Claude in our benchmarks

#235
post #55

Earlier quoted context omitted.

8 X RTX6000. It will run you around 80-100k to get started with a model at this size with decent tps.. Don't worry though, open source evangelists will tell you that these will be running on your phone in the next 3 years. For $100k you could run this model 24/7 through open router with 10 concurrent sessions at 50tps for a decade and have money left over for a vacation. There's no point in investing this type of mon…

>Don't worry though, open source evangelists will tell you that these will be running on your phone in the next 3 years. Not sure if you're being sarcastic, but I can run a quantised version of Gemma or Qwen on my 16GB M1 Macbook Pro that beats GPT-4 from 2023 hands-down. I wouldn't be surprised if, in another 3 years, you'd be able to run something as powerful as Opus 4.5 or GLM-5.2 on standard consumer hardware - s…

This is clearly where the industry is going, imho. Everyone who is playing with LLMs wants a laptop with enough grunt to run a decent model locally.

We've been sat with basically the same PC specs for ~20 years - our current specs are within an order of magnitude of the ones we could buy back in 2010. This is not really constrained by tech, as we could have much, much, larger machines. It's more because there's no mass demand for much, much, larger machines - if it's big enough to run Office apps or VSCode then you're good to go. The exponential growth we saw in the 90's was driven as much by software demand as it was by hardware development.

I can see the next 10 years produce the same kind of push for larger machines that the 90's did. And we should probably expect the same kind of standards churn as our existing technologies for storage, memory, etc, don't scale up enough and new technologies become worth developing because there's demand for them.

Re: GLM 5.2 beats Claude in our benchmarks

#237

I added GLM 5.2 to my security bug hunting benchmark when it came out, and found it to be a good performer, but not the best open model. The benchmark tests whether models can find bugs Mythos found. The best open models in the initial benchmark were DeepSeek V4 Pro or MiMo 2.5 Pro. But it turned out MiMo got lucky, it's performed worse on almost every test I've done since, while DeepSeek has consistently been among…

We need a benchmark of independent community sourced benchmarks! …probably already is one

It's not super scientific, but I really like to watch Bijan Bowen's videos on Youtube. I think he's pretty fair about the way he compares them, and it's enough for what I'm doing.

Re: GLM 5.2 beats Claude in our benchmarks

#238

Earlier quoted context omitted.

I have also found deepseek flash beat pro in some of my own internal evals for tasklet.ai it’s really surprising and I don’t understand it

The rumour is that it's trained on Opus, but who knows

Oh of course all deepseek and glm are. Multiple people have seen GLM self report that it is claude, which makes it super obvious.

I think the surprising thing is I expect flash to be a pure distillation and strictly worse quality but clearly it’s more nuanced than that.

Re: GLM 5.2 beats Claude in our benchmarks

#239

I added GLM 5.2 to my security bug hunting benchmark when it came out, and found it to be a good performer, but not the best open model. The benchmark tests whether models can find bugs Mythos found. The best open models in the initial benchmark were DeepSeek V4 Pro or MiMo 2.5 Pro. But it turned out MiMo got lucky, it's performed worse on almost every test I've done since, while DeepSeek has consistently been among…

GLM 5.2 and DeepSeek v4 Pro seem to approach security research differently. This benchmark was with GLM 5.1, but the patterns are similar: https://dualuse.dev/posts/deepseek-v4-thinks-different Overall, I still think GLM 5.2 is the much stronger performer. It's hard to tell the difference between GLM 5.2 and Opus at <120k tokens.

I have found that some models consistently find or miss specific bugs, and which bugs are hard don't completely line up across all models, so I believe that. I just refactored the security bug-finding harness I've been working on completely (not checked in yet, testing it currently) to strongly encourage "multi-model, multi-pass" scans and make them easy to orchestrate with de-dupe and weeding false positives with a strong model, rather than one model or doing just one pass over each file. Giving a model a second attempt increases their findings by 20-30%, and giving them a third, adds another 10-15%.

I'm inclined to use DeepSeek V4 Pro the most, because it is consistently extremely strong, it's very fast, it's very cheap and has excellent caching and cheap-as-free cached input tokens (something like 80% of token usage is cached when I'm using it for security scanning). So, my probably "pair" of frontline security researchers will probably be DeepSeek V4 Pro and Gemma 4 31B self-hosted (another shockingly strong contender, competitive with the best models once you let it loop on the same file a couple/few times). But, I won't be surprised if GLM 5.2 turns out better than DeepSeek V4 Pro...it costs quite a bit more.

Re: GLM 5.2 beats Claude in our benchmarks

#240
Twice in the text quotes Claude Code's F1 score as 32%, but the table shows the score is 37%. It's very likely that the actual score is 32% (because it is referenced 2 times, and a third time indirectly as the difference 'seven').

Oddly, this is a strong indication of the text being hand-written rather than LLM-assisted; it's very likely that a human made a mistake in creating the table.

  > ... beating Claude Code (32%) ...

  > ... GLM 5.2 ... beat Claude Code by seven points (39% vs. 32%).


  > Rank | Configuration           | Harness         | F1
  > ...
  > 4    | Claude Code (Opus 4.6)  | Claude Code SDK | 37%
Post reply on HN