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GLM 5.2 beats Claude in our benchmarks

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Re: GLM 5.2 beats Claude in our benchmarks

#371

Earlier quoted context omitted.

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…

So its like run 3 loops of “here project, find bugs” with all good models, then dedupe and priorize with a sota?

The loop is "look at this file in this repo, find bugs" iterated over every file in a project, with the ability to look at the rest of the repo for cross-file bugs related to the file they're instructed to look specifically at, but yes. The Anthropic folks have basically said that's how they're doing security audits (Nicholas Carlini is an Anthropic employee and he's done talks about it), so I assume that's how Mythos found its bugs.

I've benchmarked it, and the "here's a repo, find bugs" approach finds far fewer bugs. Like, dramatically fewer. Models are good and contexts have expanded, but focus still wins with hard problems. You could probably tell the good models to make a plan to audit the repo, and it would end up making its own "loop" in the form of a checklist of files to look at over several sessions or via subagents, I assume.

Re: GLM 5.2 beats Claude in our benchmarks

#372
post #262

Earlier quoted context omitted.

> but if you only want to use the best model available, it isn't there yet I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price. And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way mo…

I think people are grouping into two flows. One group is trying to get the LLM to basically one shot everything and not properly reviewing the output. Others are using the LLM to assist their human intelligence in a tight loop. If you’re doing the former you really do need the best model available because that’s still right on the edge of what LLMs can do at best, and at worst you’re just shipping pure unmaintainable…

Well put. I belong to the latter group as I feed small, granular tasks that I describe thoroughly to the LLM. I tried, however, to just give it a bigger scope task. Even best models produce sloppy code.

While the single functions/classes/structs/... can be well though out the code tends to lack cohesion, and especially maintainability. For instance, it never thinks: "I could put this logic in an interface/trait so that if the requirements change I can simply add a concrete implementation that satisfies the new requirements (and potentially use one of these for testing)".

Re: GLM 5.2 beats Claude in our benchmarks

#373

About running models locally and why data centers win (for now): they can stream the model weights to many neural engines at the same time, so each of these only needs enough RAM to hold the KV cache. So each engine is cheaper to operate, plus they are time-shared, resulting in massive wins for data centers. So one can see businesses owning their own such cluster, next to their database infra, in the near future.

Would you recommand some ressources about how multiple neural engines are used in data centers ?

Re: GLM 5.2 beats Claude in our benchmarks

#376
I don't feel the numbers without the harness are useful.

People will use the model with the harness. I know that harness may not be optimized to this model, but it's still more useful to see the numbers from an imperfect harness than from a no harness setup.

Re: GLM 5.2 beats Claude in our benchmarks

#377
post #288

Earlier quoted context omitted.

Z.ai does not lock you in to any harness.

Is there a secure way to use GLM without spending $10K’s for local HW? I “only” have a 128GiB inference machine, and don’t really trust anthropic not to steal my IP over time. I see no reason to trust Z.ai more than other vendors.

You'd need to multiply that $10k by 8 minimum.

Re: GLM 5.2 beats Claude in our benchmarks

#379
post #59

I have taken another look on these open models after the fiasco of Fable and GPT 5.6 this weekend and... GLM-5.2 truly is a good workhorse model for daily programming. I consider myself a heavy user of LLMs and a seasoned developer. A typical session for me with GPT is usually over a hundred dollars... This weekend I programmed a matrix bot with encryption and a Rust agent with some tools. Because I need one and Open…

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 is Sonnet 4.6 ranked higher than Opus 4.6?

Re: GLM 5.2 beats Claude in our benchmarks

#380
post #262

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…

> but if you only want to use the best model available, it isn't there yet I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price. And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way mo…

It's also geeks and engineers using these models and being the most vocal. We always think we're special and need the extra horsepower. Ever been on one of those home lab subreddits ? Same story.
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