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Hey. I am on a GLM Coding plan subscription (old price; their base coding plan) right now and share the key (this will go away soon).

I was thinking of going with a subscription of Claude or Codex. The reason (at least that's what I am assuming): with OpenRouter or any PAYG per token setup there will be the anxiety of using up all the tokens in days or maybe 1-2 weeks instead of a month (say I set myself a budget of 15-20 USD per month, average equivalent of a usual subscription price).

Now I don't really want the top-notch models for the coding work I do.

So how much worth of "work/tokens" will I reasonably get for ≈$20 USD if I use it a lot? How much does that equal to - or is equivalent to, say in the world of subscription based Claude, Codex, or even GLM (with their 5-hour and all those cooldowns/limits)?

I am looking for a mental model/framework to visualise this. Can you (or anyone else reading this) please point me to a source where I can get some idea about this? I know I can just add $5 on OpenRouter and try to test. But I don't really know what/how to test these spends. I also want to understand how all this works. (I am new to agentic/llm world/coding, 2-3 months, after a career break of ~3 years, that too after working for more than a decade. I know, not at all good timing!)



A good resource is this: https://artificialanalysis.ai/?cost=intelligence-vs-cost-per...

Go to the "Cost" -> "Intelligence Index vs. Cost per Intelligence Index Task" That diagram maps their "Intelligence" score to "cost per task" and I think this gives a good basis on deciding with which model you want to go. Then you can either get an API token from that models provider directly or use openrouter and set openrouter to the model/providers of your choice.

You can also see on openrouter itself the details for each model like prices and what providers are offering it at what price.

Finally you can compare models details using openrouters compare feature like this:

https://openrouter.ai/compare/anthropic/claude-opus-5.5/open...


This is very helpful. Thank you!




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