I ended up going with a Synthetic subscription (they host some open-weight models including K3 and GLM 5.3). They're the only subscription that I found that's simultaneously cheaper than token rates, and is extremely transparent about usage and pricing.
How does that make economical sense from the side of synthetic though? (please feel free to correct me if I am wrong, I usually am)
If they're simultaneously cheaper than token rates but couldn't they have sold these tokens in the first place on a platform like openrouter?
And the platforms that are hosting these tokens on openrouter aren't really earning any money at all in the first place as that itself is a race to bottom (and its more so just neutral, neither positive nor loss or barely profitable) so unless synthetic is actively making loss, and if that's the case, then it might not be sustainable, I don't quite understand the economics behind it.
Is the economics behind this the fact that since its a single subscription instead of token based pricing. Some people might use less tokens than what their subscription costs and so it offsets/subsidizes the other set of people who need more tokens.
But then, this means that as you had suggested in the first place, it would be a good deal for the people who want more tokens, so they would flock even more to the platform and it should eventually balance out unless external money is being burnt as I had said earlier.
I am genuinely interested in how they make this work so I would love if someone can shine some more light to it as I would like to read more about its economics that makes it work, thanks in advance! :-D
It appears, from the outside, to be a collection of small optimizations:
- The way they pace and limit usage seems to echo some strategies I've seen to reduce the impact of burst usage
- They cut down the maximum context length of most models
- They claim to have some custom sauce that improves how they handle API requests and reduces error rates
- Their explanations appear focused around optimizing concurrency in the same way that the 'flex' mode of some APIs does, though I've not seen specific docs of them claiming that this is part of how they save on costs
There may be more that I haven't seen or noticed. I don't work for or with them.
bhelkey · · focus · HN ↗
Does anyone see the usage limits? I don't see anything indicating how much usage is in each tier.
I checked:
* <a href="https://chatgpt.com/pricing/" rel="nofollow">https://chatgpt.com/pricing/
* <a href="https://help.openai.com/en/articles/9793128-about-chatgpt-pro-tiers" rel="nofollow">https://help.openai.com/en/articles/9793128-about-chatgpt-pr...
* <a href="https://chatgpt.com/#pricing" rel="nofollow">https://chatgpt.com/#pricing (signed in plan upgrade page)
mstaoru · · focus · HN ↗
thimabi · · focus · HN ↗
octoberfranklin · · focus · HN ↗
Scene_Cast2 · · focus · HN ↗
Imustaskforhelp · · focus · HN ↗
If they're simultaneously cheaper than token rates but couldn't they have sold these tokens in the first place on a platform like openrouter?
And the platforms that are hosting these tokens on openrouter aren't really earning any money at all in the first place as that itself is a race to bottom (and its more so just neutral, neither positive nor loss or barely profitable) so unless synthetic is actively making loss, and if that's the case, then it might not be sustainable, I don't quite understand the economics behind it.
Is the economics behind this the fact that since its a single subscription instead of token based pricing. Some people might use less tokens than what their subscription costs and so it offsets/subsidizes the other set of people who need more tokens.
But then, this means that as you had suggested in the first place, it would be a good deal for the people who want more tokens, so they would flock even more to the platform and it should eventually balance out unless external money is being burnt as I had said earlier.
I am genuinely interested in how they make this work so I would love if someone can shine some more light to it as I would like to read more about its economics that makes it work, thanks in advance! :-D
Qwertronix · · focus · HN ↗
- The way they pace and limit usage seems to echo some strategies I've seen to reduce the impact of burst usage
- They cut down the maximum context length of most models
- They claim to have some custom sauce that improves how they handle API requests and reduces error rates
- Their explanations appear focused around optimizing concurrency in the same way that the 'flex' mode of some APIs does, though I've not seen specific docs of them claiming that this is part of how they save on costs
There may be more that I haven't seen or noticed. I don't work for or with them.