I like to compare models with a similar score on cost per task and output tokens per task since those measure two things I'm interested in: cost efficiency and token efficiency. Here's how GLM-5.3 compares to other models in a similar score and against GLM-5.2 to save a few clicks for others who care about these metrics:
Model Score Cost / Task Output Tokens / Task
-------------------------------------------------------------------------
GLM-5.3 (max) 59.5 $0.68 41,107
GLM-5.2 (max) 53.0 $0.56 32,200
Claude Opus 5 (high) 61.5 $1.52 21,353
GPT-5.6 Sol (max) 60.9 $1.23 16,879
Grok 4.6 (high) 60.9 $0.84 21,735
Kimi K3 (max) 59.7 $0.84 25,474
GPT-5.6 Sol (xhigh) 59.0 $0.87 11,098
Claude Opus 5 (medium) 58.6 $0.98 12,459
Qwen3.8 Max 58.1 $1.13 38,287
Qwen3.8 2.4T A95B 57.7 $0.95 32,472
Claude Opus 4.8 (max) 57.3 $1.65 33,557
GPT-5.6 Sol (high) 57.3 $0.52 7,545
Muse Spark 1.2 (xhigh) 56.8 $0.40 30,430
GPT-5.6 Terra (max) 56.6 $0.51 20,838
GPT-5.5 (xhigh) 56.3 $0.69 16,893
Gemini 3.7 Flash (high) 56.0 $0.40 36,847
Edited for accuracy and more models.
sourcecodeplz 1 days ago [-]
Muse Spark has a nice balance. not to mentions the Contribs version is old deepseek flash prices.
glub 1 days ago [-]
Tested muse spark 1.2 because it was rated so high on design arena, and I've missed a model that can do nice UI in the hands of an operator with no UI skills.
It produced worse UI mockups than GPT and GPT models are already the bottom of the barrel here. The only model that performed well was Kimi K3 - insanely good, but expensive.
It's hard to trust benchmarks these days.
samtheprogram 1 days ago [-]
If you just want it to generate UI out of nothing, the benchmarks aren't really for that.
If you want to generate a UI based on specific user input of some kind, then they are.
I'd suggest using one model for UI and another model for tacking onto that UI. LLMs are great at pattern matching, and benchmarks don't really capture one-shotting desirable UI.
That said, benchmaxxing is a thing and your experience with models is a thing. Benchmarks are fuzzy and should be taken with a grain of salt.
sscaryterry 1 days ago [-]
I found the sweetspot here: GPT-5.6 Sol (high) 57.3 $0.52 7,545
(Edit: TLDR; It gets on with it, makes the same mistakes you would, without overthinking and overengineering, most of the time)
dudeinhawaii 1 days ago [-]
It would make reading and comparing a bit easier if the data was sorted by a dimension.
ac29 1 days ago [-]
Cost per task:
Model Score Cost / Task Output Tokens / Task
-------------------------------------------------------------------------
Muse Spark 1.2 (xhigh) 56.8 $0.40 30,430
Gemini 3.7 Flash (high) 56.0 $0.40 36,847
GPT-5.6 Terra (max) 56.6 $0.51 20,838
GPT-5.6 Sol (high) 57.3 $0.52 7,545
GLM-5.2 (max) 53.0 $0.56 32,200
GLM-5.3 (max) 59.5 $0.68 41,107
GPT-5.5 (xhigh) 56.3 $0.69 16,893
Grok 4.6 (high) 60.9 $0.84 21,735
Kimi K3 (max) 59.7 $0.84 25,474
GPT-5.6 Sol (xhigh) 59.0 $0.87 11,098
Qwen3.8 2.4T A95B 57.7 $0.95 32,472
Claude Opus 5 (medium) 58.6 $0.98 12,459
Qwen3.8 Max 58.1 $1.13 38,287
GPT-5.6 Sol (max) 60.9 $1.23 16,879
Claude Opus 5 (high) 61.5 $1.52 21,353
Claude Opus 4.8 (max) 57.3 $1.65 33,557
Benchmark score:
Model Score Cost / Task Output Tokens / Task
-------------------------------------------------------------------------
Claude Opus 5 (high) 61.5 $1.52 21,353
GPT-5.6 Sol (max) 60.9 $1.23 16,879
Grok 4.6 (high) 60.9 $0.84 21,735
Kimi K3 (max) 59.7 $0.84 25,474
GLM-5.3 (max) 59.5 $0.68 41,107
GPT-5.6 Sol (xhigh) 59.0 $0.87 11,098
Claude Opus 5 (medium) 58.6 $0.98 12,459
Qwen3.8 Max 58.1 $1.13 38,287
Qwen3.8 2.4T A95B 57.7 $0.95 32,472
Claude Opus 4.8 (max) 57.3 $1.65 33,557
GPT-5.6 Sol (high) 57.3 $0.52 7,545
Muse Spark 1.2 (xhigh) 56.8 $0.40 30,430
GPT-5.6 Terra (max) 56.6 $0.51 20,838
GPT-5.5 (xhigh) 56.3 $0.69 16,893
Gemini 3.7 Flash (high) 56.0 $0.40 36,847
GLM-5.2 (max) 53.0 $0.56 32,200
salamo 1 days ago [-]
This matches my experience with Sol. Read and thought for a while, and edited files, tested, edited again, then ran out of budget in a relatively short time. But its solution was very good and was done quickly, so all things equal I prefer that over something much more verbose like Deepseek.
tw1984 1 days ago [-]
this is not very useful.
for over 1 billion real world users living in China, they don't have the option of paying $1.52 per task to use Opus 5, they are banned doing that due to US politics.
Barbing 1 days ago [-]
What’s the reseller situation?
teravor 1 days ago [-]
these $/task figures aren't very useful in my experience. it doesn't tell you how well it did the task.
generally I choose models by their intelligence and then personal preference from direct experience.
kolinko 1 days ago [-]
they make a ton of sense for tasks that are achievable with no prob by all models - e.g. writing scripts that do a specific thing etc.
teravor 1 days ago [-]
> writing scripts
you can use a Gemini model completely for free doing that. no agent. aistudio.google.com
glub 1 days ago [-]
I've tested GLM 5.3 on the release day and Artificial Analysis is spot on. It's a really good model.
But my main takeaway was something else. I've used closed weight models for long enough that I've forgotten how good it feels to see reasoning tokens.
With GPT/Claude, you kind of hope that intent was captured well, that agent had all the information, all the tools it needed, because you won't see "hmmm it seems like nix flake isn't available here and I shouldn't install something globally" until it slopped out millions of tokens and wasted hundreds of dollars for 8 hours. With GLM and the likes, you just stop the disease right where it begins.
Havoc 1 days ago [-]
Yes, not necessary often but being able to stop something that is going off the rails is super useful. Especially if the root cause is prompt ambiguity - inject a clarification & it recovers
glub 1 days ago [-]
It's also starting to go beyond reasoning and it's becoming much more problematic. Reasoning is one thing, but codex, for example now encrypts agent-to-agent messages as well, and compaction. I've no idea what subagents are instructed to do, or what they reported back in native codex.
The only thing that's keeping me is the value $200 subscription provides. If that value disappears, I see no reason why not to switch to something that isn't a black box.
aitchnyu 20 hours ago [-]
Generally, are closed sourced models hiding their traces? I was making an agent to develop and deploy apps and fed the traces to dispel time-consuming detours and made it a few times faster.
tw1984 1 days ago [-]
With GPT/Claude, hiding those from users to waste their tokens is a feature, not a limitation.
Escapade5160 1 days ago [-]
Sol is an underappreciated model. Dropped Claude today and went to codex. None of that god awful prose Claude used for me any longer.
The Chinese models also like to cut corners on stuff like science. Their scores on stuff like biotech and scientific knowledge is far from ChatGPT unfortunately. (Claude is pretty good but it just refuses all prompts).
AnodicElegy 1 days ago [-]
I understand that running these benchmarks can get expensive, but it would be really nice to see AA include more benchmarks of models at reasoning settings other than the maximum, at least for the biggest releases. They have that nice graph of cost vs. composite benchmark score with the Pareto frontier line, but who knows if those are actually the optimal choices? There are already a few non-max-reasoning models on the Pareto line, among the few that were tested.
apitman 1 days ago [-]
You can turn on various levels of some of many of the models in the UI
AnodicElegy 23 hours ago [-]
Yes, they have multiple levels of Claude, GPT, Gemini, and Kimi, but not the other top models (I would put GLM, Qwen, Muse, Grok, and Deepseek in that bucket).
Zaheer 1 days ago [-]
Is it worth using these models if I have a claude code subscription already? The appeal of lower cost is nice but I haven't gotten over the switching cost yet.
robertn702 1 days ago [-]
FYI, you can use your Claude subscription pricing with OpenCode via Meridian[0], which also makes it easier to try out other models when they come out. You can also use your other subscriptions in OpenCode with CLIProxyAPI[1]. The switching cost was relatively high, mostly from claude code plugins but completely worth it. I'm now mostly using GLM-5.3 and Codex models via OpenCode and barely using Claude which seemed unfathomable less than two months ago.
I'm sometimes tempted to use these sorts of proxies, but I always worry that the hacks they have to use to wrap the upstream tools and APIs is going to mess with my downstream harness/agent.
culi 1 days ago [-]
Use a unified proxy that lets you switch between models seamlessly. We are far from an equilibrium in this market and you will continue to have FOMO no matter who you pick if you go all in on one company
I use the $200 plan w/ Anthropic and run out of tokens half way through the week and supposedly they are progressively reducing the limits on all their subs even further.
At some point I will switch, $200 buys a lot of tokens on OpenRouter.
Sabinus 1 days ago [-]
Is the conventional wisdom that the subscription price/token is better than the API price/token not valid any more? Or is access to model diversity worth the increased per token costs?
1 days ago [-]
kilroy123 1 days ago [-]
Same here. I'm switching to the Codex plan. It just doesn't go very far now. Especially if you use fable at all.
colingauvin 1 days ago [-]
At least by API usage, they aren't yet lower cost than subscriptions. Not sure about GLM's subscription plans though.
glub 1 days ago [-]
GLM subscription is better than API, but significantly worse than Codex, even when used outside peak hours.
glub 1 days ago [-]
If anything, it's going to be more expensive. Price/performance ratio isn't there yet for frontier open weight models.
But regardless, you definitely should use a harness where switching models on the fly is easy. There's a reason why Anthropic uses their own proprietary formats/conventions anywhere they can - to lock you in when inference eventually commoditizes.
notatoad 1 days ago [-]
no, at subscription prices claude is a better value than GLM.
They're only a better value if you're paying API rates
scotty79 23 hours ago [-]
You can have a subscription for GLM.
notatoad 7 hours ago [-]
yes, but the GLM subscription gives you barely more usage than you'd get just paying API rates. it's not subsidized like the claude or chatgpt subscriptions are.
swingboy 1 days ago [-]
Does Artificial Analysis use OpenRouter for model access to do their benchmarks?
sumedh 13 hours ago [-]
I ran the same Mac SVG drawing prompt through GLM 5.2 and 5.3 across every reasoning effort level, and 5.3 showed improved performance
Very impressive score for the size, though token use is higher than k3 and far higher than proprietary models, and its price to performance isn't all that far ahead of k3 as a result
Havoc 1 days ago [-]
>token use is higher than k3 and far higher than proprietary models
GLM sets effort to max by default historically.
markasoftware 1 days ago [-]
Aa also benchmarked k3 at max
yipinwong 1 days ago [-]
Still yet, I cannot justify switching from dirt-cheap Luna model, which is pretty damn "intelligent" and works well for my flow
colingauvin 1 days ago [-]
Tied for #1 by agentic index (with Opus 5).
gdorsi 1 days ago [-]
To me one of the biggest limitations of GLM is the lack of multi-modality.
For web dev is just a must to have, and offloading that part to a secondary model doesn't work really well in my experience.
scosman 1 days ago [-]
And reminder: it's less than a quarter the size of Kimi K3!
It produced worse UI mockups than GPT and GPT models are already the bottom of the barrel here. The only model that performed well was Kimi K3 - insanely good, but expensive.
It's hard to trust benchmarks these days.
If you want to generate a UI based on specific user input of some kind, then they are.
I'd suggest using one model for UI and another model for tacking onto that UI. LLMs are great at pattern matching, and benchmarks don't really capture one-shotting desirable UI.
That said, benchmaxxing is a thing and your experience with models is a thing. Benchmarks are fuzzy and should be taken with a grain of salt.
(Edit: TLDR; It gets on with it, makes the same mistakes you would, without overthinking and overengineering, most of the time)
for over 1 billion real world users living in China, they don't have the option of paying $1.52 per task to use Opus 5, they are banned doing that due to US politics.
generally I choose models by their intelligence and then personal preference from direct experience.
But my main takeaway was something else. I've used closed weight models for long enough that I've forgotten how good it feels to see reasoning tokens.
With GPT/Claude, you kind of hope that intent was captured well, that agent had all the information, all the tools it needed, because you won't see "hmmm it seems like nix flake isn't available here and I shouldn't install something globally" until it slopped out millions of tokens and wasted hundreds of dollars for 8 hours. With GLM and the likes, you just stop the disease right where it begins.
The only thing that's keeping me is the value $200 subscription provides. If that value disappears, I see no reason why not to switch to something that isn't a black box.
[0] https://github.com/rynfar/meridian
[1] https://github.com/router-for-me/CLIProxyAPI
edit: reworded for clarity
At some point I will switch, $200 buys a lot of tokens on OpenRouter.
But regardless, you definitely should use a harness where switching models on the fly is easy. There's a reason why Anthropic uses their own proprietary formats/conventions anywhere they can - to lock you in when inference eventually commoditizes.
They're only a better value if you're paying API rates
https://sumedh.info/models/glm-5-3
GLM sets effort to max by default historically.
For web dev is just a must to have, and offloading that part to a secondary model doesn't work really well in my experience.