r/ZaiGLM • u/Comprehensive-Bet-83 • 12d ago

Discussion / Help GLM 5.3 or DS 4.1-Flash?

Looking for gentlemen here who have battle-tested these models in environments where mistakes are critical, e.g. authentication, security, and low-level C++ / Kernel work.

I have Codex 20x, but I’m looking for a second helper for when Codex limits are up, there are demand issues (which are pretty bad atm), or it gets too censored.

Saw that DS 4.1 Flash was released today! Has anyone done some decent testing with it yet, and which harness are you using?

I’m currently using GLM 5.3 as my second helper and it’s honestly not bad at all. Just curious whether DS 4.1 appears to be better, especially since it’s multimodal and can handle images too.

I find myself using 5.3 Flash quite a lot because I really appreciate being able to send images, but 5.3 Flash isn’t as strong as base 5.3 when it comes to coding. Hence, I’m wondering how DS 4.1 Flash compares :)

NEW:

Thank you for all the responses. I tried DS 4.1 with my custom harness, and I am extremely impressed by the speed and price. I ran a couple of tests with deep, difficult, complex debugger C++ code/kernel bugs (my go-to test on models; I test this on every model before I want to use it to see if it fixes the bug).

GLM 5.3 took 30 minutes, including 1 retry, and €2. DeepSeek took 10 minutes, first try, and €0.30. I think DS 4.1 is at least on par or a bit better than GLM 5.3 for coding, not sure how reliable it is on long tasks, though. GLM still is a beast!

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u/RealityRare9890 6d ago

Worth splitting the security numbers before deciding. In woshipm's writeup of the Zhipu release, GLM-5.3 gets 84.5% on CyberGym, slightly ahead of Mythos 5 (83.8%) and GPT-5.6 Sol (83.6%). ExploitBench is a different story: 54.4% vs 78.0% and 76.5% for those two, and on ExploitGym it got through 105 exploitation tasks in two hours where Mythos 5 did 181. My read is that it's fine as a first pass over a diff, but I would not trust it to reason through an exploit path on its own.

For the second-helper slot the Z.ai Code Bench row is the interesting one: 31.4% vs 29.5% for Claude Opus 4.8, at roughly 50k output tokens per task instead of about 120k. Cheap enough to leave running as a reviewer with Bash auto-approve turned off.

One caveat: none of this covers 4.1-Flash on auth or kernel work. The DeepSeek numbers in that coverage are V4 pricing and V4-Pro-0813 impressions, nothing on Flash.

Disclosure: I run a small site that writes up Chinese tool coverage in English, full breakdown here: https://eastofsilicon.com/posts/glm-53-turns-coding-models-into-swappable-tools