i see all these claims its too expensive but why arent we comparing it to wage costs, is $10-20 an hour actually expensive?
Why current LLM costs are not sustainable
81–90 of 216 posts
Re: Why current LLM costs are not sustainable
#82> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. If you can use a subscription with any of the SOTA models, do that. Instead of around 4k EUR in token costs, my Opus usage costs me 108 EUR (with taxes) per month with their Max 5x plan. It's the same with OpenAI, those are heavily subsidized. It doesn't make sense to pay per-token, unless you must. > Wh…
Opus 4.8 High effort seems adequate for me currently, at API pricing, with a $200/month budget. This is at work where I don't work on greenfield or parallelize feature development. I cannot see the agent burning through $50 for one moderately sized TypeScript cleanup in my setup. This sounds like something that can be improved on OP's side. There have been rumors about a potential Sonnet 5 model release in the near f…
> I cannot see the agent burning through $50 for one moderately sized TypeScript cleanup in my setup.
I have absolutely seen stuff like this happen. Think about it, when you point Claude at a bunch of files, it has to suck them all up (tens of thousands of tokens), spend some proportional number of tokens doing stuff, and spit them back out (tens of thousands of tokens) for each pass in the "cleanup" loop. I had a similar situation occur a few months ago. Very small "add Javadoc to these dozens of classes" scenario. Sonnet rapidly rate limited my $20 plan so I switched to extra usage. A very small (IMO) number of changes later I had spent like $7 in tokens.
The main problem is you really have no idea ahead of time just how many tokens a given task is going to take. I suggest you try spending a day running your Opus 4.8 High effort on API pricing to see just how much your $200 subscription is being subsidized before you confidently state that $50 for some TS cleanup task isn't possible.
Re: Why current LLM costs are not sustainable
#83I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts. So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM'…
What is a "normal" subscription? Are you using Claude Code, or just Claude??
Re: Why current LLM costs are not sustainable
#84> GPT 5.5, for example, costs $5 per million input tokens and $30 per million output tokens. This is currently the costliest model available as per OpenRouter.
claude 5 mythos and fabled are 50$/MOutTok. Previous models were priced at 75$, so presumably they found the "too expensive" price point.
Re: Why current LLM costs are not sustainable
#85Earlier quoted context omitted.
>3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that. This idea that the subscriptions are subsidized is repeated over and over, but I've never seen any proof of this. It seems to be entirely based on the inferred API cost the subscription usage could give you, but…
They are subsidized by the huge losses incurred by the AI companies.
Only if those losses are coming from subscriptions, instead of capex and training, which is not at all clear.
Re: Why current LLM costs are not sustainable
#86The problem space has a few aspects: 1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs. 2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen. 3. W…
>3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that. This idea that the subscriptions are subsidized is repeated over and over, but I've never seen any proof of this. It seems to be entirely based on the inferred API cost the subscription usage could give you, but…
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Re: Why current LLM costs are not sustainable
#87I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts. So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM'…
I mean have you tried to tokenmax?
It is not that hard. Just launch 10 different windows and make sure to loop back in after every turn and you will be burning billions of tokens per month in no time.
Re: Why current LLM costs are not sustainable
#88There is a wave of users switching over to DeepSeek Flash. There are Reddit threads of users sharing billion token spend for $20. If all of global spend on Anthropic/OpenAI/Gemini APIs just switches over to DeepSeek then easily we can decrease total AI spend by 10x
Probably won't be too long before the government decides to block deepseek's website based on "security" concerns.
It’s in the doing with the DAAMT Act and soon foreign AI companies will be on the entity list. Circumvent this sanctions and count with assets forfeiture, civil penalty and criminal prosecution. This will eliminate access to Deepseek and so on overnight. Cherry on top most westerners will face similar problems due to secondary sanctions.
Re: Why current LLM costs are not sustainable
#89The problem space has a few aspects: 1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs. 2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen. 3. W…
> 2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen.
It's actually worse, the AI explosion just hikes hardware prices faster than capacity can catch up - and it will likely not catch up in a while because investments are both expensive, long, and might not seem all that good idea while bubble is still bubbling.
The massive push frankly also made it unsustainable. If RAM didn't cost 3x and compute manufacturers would have to compete instead of selling every unit instantly at whatever price they want the frontier model tokens might've costed closer to sustainable amount
Re: Why current LLM costs are not sustainable
#90I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts. So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM'…
Same for me, comfortable with a single Max sub, launching "claude --effort max" with Opus 4.8 (alas poor Fable, please come back!).
But try running Claude Opus at API prices through a 'clever' RAG based intermediate system 'managing' a 2024-era context size window completely unaligned with 2026 frontier model tool use expectations, that results in 100% cache miss and content coherency destruction on every single interaction. There's your typical 'Enterprise Agreement' GenAI setup.
I only really discovered this when trying to find out how my Enterprise friends' AI experiences were so completely opposite from my own successes as I could not believe how poor their results were even though on the surface it looked like we were using the same model, and I know they aren't 'bad' software engineers and developers.