GLM-5.3-Flash vs DeepSeek-V4.1-Flash
I ran the exact same tasks on both on an agent harness I'm building:
- Code review
- Mobile review
- Improvement plan
Then I verified every claim from each model against the code. Who's the winner?
▶️ On code review, GLM 5.3 Flash won.
GLM finished in 27 minutes and worked by reading 35 file reads, and performed 27 searches.
DeepSeek took 45 minuted and worked by running 55 shell commands.
DeepSeek raised 23 issues, GLM raised 10.
Both were right about roughly 60% of the findings. So DeepSeek found more real problems, and more fake ones.
That's not what decided it though.
GLM's biggest finding was correct. DeepSeek's biggest finding was wrong, and that is why GLM 5.3 Flash won on code review.
▶️ On mobile review, DS4.1F won.
It found a bug that makes the composer unusable the second messages start queuing. GLM had that exact component on screen, asked the wrong question about it, and moved on.
That said, my prompt explicitly instructed both models that look and feel were super important. GLM viewed 14 screenshots. DeepSeek viewed zero.
▶️ What about the improvement plan? This one goes to GLM 5.3 Flash.
GLM was broader and better prioritized. It also came back with 33 improvements, each one sized and properly prioritized.
DeepSeek v4.1 Flash came back with 14. Deeper on a couple of them, but it spent almost nothing on UI and performance. Two of the four things I asked for.
Different instincts too. GLM went hunting for things already half built and never wired up. DeepSeek went narrow and quantitative and argued that I should first build a way to measure improvement before acting on any of its own ideas.
On this task, both were accurate. I verified every claim.
So which is better?
🥇 GLM 5.3 Flash takes it 2-1. Better calibrated, roughly 1.3-1.7x faster, and it knows when its own tooling is the problem.
🥈DeepSeek v4.1 Flash feels "sharper" and the one most likely to be confidently wrong about what it just measured.
For me GLM 5.3 Flash is the one I run daily.