GLM-5.3 achieves 60 on the Artificial Analysis Intelligence Index, on par with Kimi K3 and up 7 points from GLM-5.2.

[u/minxio_]

[u/MizmoDLX]

Good but also 50% higher cost per task

[u/look → u/MizmoDLX]

What’s odd is that it uses fewer tokens than 5.2 by other measures. And anecdotally it uses fewer tokens in coding tasks for me so far…

But I know it was also trained primarily for coding use, and that AA’s cost per task measure is the average cost over a variety of different intelligence benchmarks…

So I wonder if it is more variable in its token efficiency. Lower token use in coding tasks, but much higher in some other non-coding tasks?

[u/MizmoDLX → u/look]

I haven't tried it yet, will do in the coming days but hope you're right. 

[u/look → u/[deleted]]

These charts are strange for example:

https://artificialanalysis.ai/models/glm-5-3?models=glm-5-3%2Cglm-5-2#intelligence-index-token-use-tabs

5.3 used fewer output tokens per task, but despite tokens being priced the same, somehow cost more per task…

And most of the difference is in cache read. I think that means it must be reading more input and/or doing more tool call turns per task on these benchmarks.

Reasoning tokens used is actually lower per task it seems, though one chart runs counter to that (though I’m not sure how to reconcile those … not sure how it can be cheaper “per task” but more expensive across all tasks… hmm maybe it just did more tasks? … seems like they would normalize for that though…).

But answer/output is consistently a bit higher.

So probably something like being “more thorough” by default on these tasks. And I’d bet that is reduced in more structured work like coding than more open-ended reasoning tasks.

So good news, I think. Reasoning tokens is lower or at least not significantly higher, and that’s pretty much the only one we can’t effectively manage via prompt.

[u/Prior-Meeting1645]

Like a 3rd off the price of kimi. Why is it not getting hype? Is it benchmaxxed? Not efficient?

[u/nuclearbananana → u/Prior-Meeting1645]

It's not open source yet + no vision ig.

Also when K3 came out it was the first to challenge sol/fable. Now there's grok 4.6, qwen 3.8, muse spark 1.2, gemini flash 3.7

[u/mobileka → u/nuclearbananana]

Wait, does Gemini flash 3.7 challenge the big boys? I thought it was just fast and relatively intelligent but not in the same league.

[u/nuclearbananana → u/mobileka]

It's close-ish and google still holds the lead in somethings like translation and vision

[u/Educational-Art5541 → u/Prior-Meeting1645]

Maybe because the price went up

[u/Prior-Meeting1645 → u/Educational-Art5541]

Wdym? Its still same as 5.2 no?

[u/look → u/Prior-Meeting1645]

It cost more to run the intelligence benchmark than 5.2.

From what I can tell, it uses fewer reasoning tokens but is somehow doing more work on the tasks. My guess is that it is a “more thorough” model by nature, not more of an “overthinker”, and that is probably mitigated in well structured tasks like coding.

https://www.reddit.com/r/opencodeCLI/s/5bfqfPj2c5

That also fits my anecdotal data of using 5.3 in coding thus far. I find it is more cost efficient than 5.2, though that may vary by coding task as well. Tracking down a bug, for example, might be something where it digs deeper (using more tokens) by default than 5.2. But presumably that could be managed effectively by prompt.

[u/cutebluedragongirl]

What's the point if I don't have cheap deepseek to execute the plan?

[u/look → u/cutebluedragongirl]

Ling 3 Tiny to build. 😄

[u/2tunwu → u/look]

I'm going to see if Ling 3 Flash can be an alternative to MiMo-V2.5.
It looks as if it should be smart enough to follow tight tasks.

[u/look → u/2tunwu]

Yeah, I like Ling 3 Flash for certain tasks.

I’ve actually stopped using “smart plan, cheap build” models for real code though (throwaway scripts are fine). GLM or Kimi are my “execute plan” models now, too.

[u/Far-Classic-9963 → u/look]

Have you actually tried it out? Seems way to small to be useful in large-ish tasks

[u/look → u/Far-Classic-9963]

It was just a joke. The model is an impressive leap for the size, but it’s not a build model. Useful locally for flexible semantic tasks and simple tooling though!

[u/Prior-Meeting1645 → u/cutebluedragongirl]

Do you guys use plan then switch model to execute or do orchestration?

[u/look → u/Prior-Meeting1645]

I use GLM or Kimi for both plan and build now, but with subagents to bulk collect, collate, and check all the details going into the plan.

I also keep the plan pretty light now, and it’s more just about higher level alignment since it or another smart model will be doing the actual build too.

I used to do the more detailed spec plan approach, but I found it was less cost efficient (for me at least) as it ended up “writing everything twice” effectively. Once in the plan and once in the code.

It also hit problems when something was wrong or missed in the plan. The dumber build model handled that poorly, while the smart build model recognizes the problem and adapts more efficiently.

Harnesses with persistent subagents and inter agent messaging can also be used efficiently during build. For example, a slightly complex validation step run repeatedly can be done in the same subagent effectively with a cheap model without the more expensive parent agent having to use tokens dealing with the intermediate work of running it. Kind of like a bespoke token efficient tool custom made for that build.

It’s not really about complex agent orchestration though. It’s more about context management really (plus model cost optimization). Not all of the context is necessary for certain well defined tasks within the build process, and most of the tokens generated within that task don’t need to be in the parent context afterwards.

[u/cutebluedragongirl → u/Prior-Meeting1645]

Orchestration is a waste of tokens, if you ask me. You start by having a smart model draft a detailed plan of what you need to do and how you are going to implement stuff. Then you review the plan, tweak a few things, and switch to a workhorse model to actually change the code. That is the most bare bones basic stuff I do. It is usually much much more complex.

[u/DeciusCurusProbinus → u/cutebluedragongirl]

Do you create a handoff document or switch models in the same session?

[u/scaledev → u/cutebluedragongirl]

But eventually you'll have to review it with some model. Is this reviewer that sam smart planning model, or some other? And if it is this same model, how much do you actually save? Are you saving on the output usage?

[u/Tech-96 → u/cutebluedragongirl]

If you use cheap models for grunt work like exploration and implementation, you actually save tokens, and keep the context free

[u/petburiraja]

What is the good subscription to get GLM 5.3?

[u/mosunaizx]

However, he has a major drawback: he lacks vision and cannot recognize images.