The timing of these price cut discussions says to me OpenAI has no imminent release that will be edging out Mythos/Fable.
If so the question becomes when can they do so, or is this possibly a turning point where Anthropic keeps the crown to themselves for the foreseeable future.
I got a new $20 Claude subscription to try the new Fable model. I gave it a single prompt, and it barely finished, using up my whole session quota (it was at ~95% when it finished) and 10% of my weekly quota.
For comparison, with the Kimi Code $40 subscription I can pretty much constantly run two/three agents in parallel for the whole week, and I never run out of quota. I can blindly throw it at anything and everything without worrying about hitting the limits. (And it's not exactly a cheap model to run -- it has 1 trillion parameters!)
Is Kimi as good as Claude? Of course not. But you don't need the absolute state-of-art for most things. If I don't have exceptionally difficult tasks it makes no sense to use it. Just throw Kimi at it, and even if it needs to run 2 or 3 times longer in the background I don't care, because I'm not running out of tokens there.
I don’t fully understand why OpenAI lacks this focus, as clearly identifying a target market is one of the first things you do with a business strategy. But instead they just seem to throw stuff against the wall and see what sticks.
It seems very competent at coding tasks as well. I don't think Anthropic has a huge edge on that front. It's more of a neck and neck race with proponents in both camps. I ignore most benchmarks at this point; I don't think they have much relevance for normal users.
I think it's actually necessary for both to try out different approaches. Nothing is set in stone yet when it comes to the UX of these things.
Resource curse: https://en.wikipedia.org/wiki/Resource_curse
I've been inside companies that have struggled with this, and the real internal story goes like this:
1. Surprise product growth
2. Revenue go brr, org expands
3. Everyone gets promoted as org expands
4. Because the product sold itself, there was little selection pressure on the sales / customer success orgs to evaluate their effectiveness
5. Leadership gets saturated with people who just aren't very good at their job
6. None of those people get fired/demoted, because the company never had to develop "What to do with a bad leader?" muscles
7. This eventually manifests as an increasing (customer) <-> (engineering) disconnect (as sales/cs aren't doing their job)
8. People begin to ask why the company isn't doing (insert obvious thing)
9. It's because VP-of-whatever is chasing fantasies instead of reporting customer needs to engineering
With that said you are right, it seems OpenAI got numbed by ChatGPT's initial success and tried to be the go-to brand for consumers... which is Google's playground.
Meanwhile, Anthropic led the B2B market with a clever segmented approach, and got well-paying customers.
Initially I had the same thought but I think this might actually have more to do with Fable being removed from the Claude subscription later this month. At that point it becomes cost prohibitive to use for most tasks anyways & this is the perfect opportunity to compete on price, especially given enterprise customers are already looking to improve spend management
Also, I don’t about others, but I personally strongly dislike OpenAI’s leadership’s hypocrisy. I find them losing the race highly satisfying.
This specific crown (Best Performing Model) appears to be made out of thorns: pay 100x more for maybe a 10% improvement in capabilities.
Not sure what the goal is, here.
It probably won't be the same again but I still think we can bet on radically cheaper Mythos level intelligence in the future.
If OpenAI can offer an alternative to Opus but with better pricing, it will boost their revenue at Anthropic’s cost, in time for the IPO.
I keep meaning to try Claude Code, but I can't seem to run out of limits on Codex on regular pro plan.
Meanwhile all my friends on Claude Code are fighting the token limits every few hours.
I even switched to using extra high for easy medium level script tasks as a test and besides taking longer there was not much reduction in the token allowance.
I generally write a detailed spec before plan then possibly iterate a bit before implementation. Not sure what I am doing "wrong".
This lets me mess around with random experiments as I "catch up" on how to find various (mostly silly) uses for the technology.
That and much less worry about being banned without warning for not using approved harnesses as I try random stuff is a giant plus as well.
I assume they have lots of spare compute and less demand than Anthropic as it's obvious they are subsidizing my usage for now. But it lets me start off with "giant context window" type playgrounds which give immediate moderate effectiveness, then I can figure out how to tighten it up and reduce token burn from there.
I imagine they track usage and can see whether their habitual users are switching to something else and aren't going to slash prices 'for the hell of it'.
just look at public stats on openrouter (obviously not indicative of first party app usage or direct api usage, but there's a huge difference between these graphs): https://openrouter.ai/openai https://openrouter.ai/anthropic
Deepseek has been on a growth spurt recently. Openai is at half and looks almost flat in comparison to anthropic and deepseek.
And, I even use `claude -p` pretty regularly for scripted stuff (automated security vulnerability searches), which I thought was now counted at regular API rates, but that doesn't seem to ever run out either. I do only run one at a time, though...not parallel, so maybe it doesn't kick over into some "automation" mode of counting usage, I dunno.
The billing change starts 15 June:
https://zed.dev/blog/anthropic-subscription-changes
(Couldn't find a direct Claude link that wasn't a Twitter tweet (what is with AI platforms making announcements on that fElon-owned platform?!), so the Zed one seemed best.)
I also hit limits if I do something important, at which point I make it do a loop with significant subagent counts to just review and adjust the code extensively using a bunch of frameworks. Im perfectly happy with the CC limits of a max plan, it is never something that blocks me.and when it runs out im brain fried as well anyway so thats not an issue.
More tokens and bigger models pre-ipo to attract attention, limit everything post-ipo.
They did it before, will do it after.
However, I think actually that while it won't give the results expected (AI agents run the company, build all features, etc.), it will nevertheless become a developer tool like IDEs, something "you have to have".
It's here to stay but probably with more realistic expectations than some CEO/CTO are pushing for (agents for everything, nobody writes 1 LOC, self healing systems, etc).
So the market expectations will be probably resized, but these tools are here to stay. Be it for cybersecurity (from CVEs to cyber warfare) alone, that's already worth all the money they are throwing a it.
These moves will only accelerate it.
"We lose money on every customer, but we'll make it up in volume" :-)
Reality is Fable is x2 price increase against previous.
GPT5.5 is x2 price increase against previous. And after the last week reset, codex is hungry for your sub allowance.
Everybody can see that the massive raises are not matching the revenue, at all.
It's a surprising headline. Yes it does make sense to cut the price to gain market share, but it also make sense to keep it at a sustainable level, which seems to not have been reached yet.
Not sure about GPT but it seems plausible they've also been increasing the model size with recent releases. (Progressively training a bigger model and easing into a profitable price range for that model scale?)
This was a week after deepseek slashed prices!
I am curious how many on HN have manually configured their copilot install with a custom OAI token for 5.4/5.5. In my experience, the performance difference over the built in subscription models is immense. This setup tends to solve the problem so quickly and reliably that any desire to have it run while I'm asleep seems absolutely ridiculous. The performance is constant throughout the day and week.
I think what might be happening is that we are chasing the cost optimization rabbit a little bit too hard. Capability is weird dimension to quantify. A weaker model is not weaker in a linear way. It's usually this incredibly tall brick wall of a discrete go/no-go. If the model can't do the task, it doesn't matter how cheap the tokens are. Something approaching the inverse is also largely true.
Focus on the capability (is this giving my customer what they want) instead of the cost, and you will likely find that the cost never reaches a threshold where you even begin to worry about it. Starting from a position of cost optimization tends to spiral into a dark place.
could that be the difference from your peers? :p (real question b/c if you brought it up you're probably seeing others do it)
[1] https://www.theverge.com/report/947575/microsoft-claude-fabl...
Maybe the better comparison is Uber? I.e. a commoditised product (taxis on an app), burning money to directly subsidise and gain market share. I always thought it was utterly insane and a waste of money... But you'd be hard pressed to have not made money on Uber.
This is my understanding anyway. A LLM-generated summary suggests that anyone who invested pre-IPO got at least 8-10% annually compounded. Even Series G investors made 2.3x since then. It's not an Eldorado and has to make up for all the losers in the VC portfolio but it's money made, not a smouldering crater of losses.
And after going public, return from IPO is 9.4% compounded. Price is 40% below all time high in October 25 but hey that's a harsh criterion for a long term investment.
The reason why I think it's a good point of comparison is that there's no moat, plenty of competition, heavily subsidised for years by literally burning cash, now seemingly profitable and a reasonably sane PE ratio of 17.
Of course one difference is that a major cost item for LLM companies is building genuinely new, cutting edge engineering/science products whereas for Uber, I never understood why they need the 1000s of technical staff to deliver a taxi app.
I don't know about the ins and outs of the business models of either LLM providers or Uber but keen to hear from people who have insights.
Not sure why people are talking about revenue and profits. Sam & co are about to make ridiculous bank.
Claude actually works - unless OpenAI can do that it would make no difference if it was free.
It works unbelievably well actually - it’s truly amazing.
I am not complaining, I like my investor subsidised tokens, I don't see what these companies see as their end goal when it's becoming more and more possible to run a competent LLM locally(even with today's RAM prices).
I am surprised that there is no Claude or ChatGPT machine that I could buy, I feel like they should be opening up that model, but I guess subscriptions look better on balance sheets.
I completely don’t understand Anthropic’s pricing where you have to pay a monthly fee to access their crappy models and pay per use for access to their top model. If you’re going to go pay per use it should be actually pay per use.
Right now OpenAI is looking like the one setup to fail here. They have lost momentum big time and are looking incredibly vunerable.
I'm doing model training and architecture dev all day. often running loops to monitor training status or executing spec and E2E tests.
I really hope token costs come down.
It would be awesome if OpenAI could double the usage allowance for the Pro subscription.
Think about where any of them will be in 20 years
On device AI?
That way you will loose money even faster and we can finally get ridd of this nonsense even sooner.
More than happy to watch them lose the global consumer market while they compete with Palantir for DoD contracts.