People say GLM 5.3 is not much smarter than GLM 5.3 Flash (just +4 points on Artificial Analysis), and GLM 5.3 Flash fits on 4x RTX 6000 Pro for 4 times less money, with the same 600 tok/sec aggregate decode speed on vLLM (6 mln tokens KV cache). And I tell you, 600 tok/sec is not much for an organization, it's barely usable for 8 people. That's why we plan to buy more RTX 6000 Pros. And this setup requires $400,000 to run GLM 5.3 for 16 people, it's absolutely uneconomical.

5.3 is certainly “better” than its flash variant, in my experience. I’ve moved on from both but eagerly await the next generation of each.

Better, in this context, is more capable with open ended work, planning, longer horizon “reasoning”. Flash is an absolute chad with a well defined task and accompanying guidance/guardrails/conventions provided. But not even close when it needs to deviate, runs into a “blocker” and has to reason its way out.

Personally I find GLM-5.3 to be smart enough to be my daily driver, writing code, doing investigations, etc., including multi-codebase changes and finding the relevant documentation/supporting code for itself so I don't have to explain everything. It's fast (depending on your inference provider), cheap, and smart enough.

I find the full-size 5.3 is smart enough to avoid all the small-model problems, while GLM-5.3-Flash definitely starts showing its limits any time the task at hand is poorly defined. 5.3-Flash can often manage even complicated tasks, but the error rate (in terms of bad decisions, misunderstandings, etc) starts creeping up and the time wasted (or the risk thereof) gets high enough that it's worth just paying for the bigger model.

I do wish the bigger model could use vision though- that's currently limited to the flash model (presumably because it came out later, when they had figured out their vision training pipeline. Hopefully later releases will include it.)

You’re not going to get the same token throughout with the pro 6000 over pcie. You’re paying for speed and bandwidth.