Codex pricing to align with API token usage, instead of per-message (opens in new tab)

I think this might also impact how usage is calculated for subscription plans as well, not just overages (using tokens instead of messages for calculating usage). But the message from OpenAI seems vague.

I miss “BREAKING NEWS” as it is used at X /s

It does feel like also impact the usage meter for subscription plans?

> Not everyone uses dollars.

> The price of credits in some currency could change after you bought them.

> The price of credits could be different for different customers (commercial, educational, partners, etc)

Maybe I'm missing something, but doesn't every other compute provider manage that without introducing their own token currency? Convert to the user's currency at the end of the month, when the invoice comes in. On the pricing page, have a table that lists different prices for different customers. I fail to see how tokens make it clearer. Compare:

"This action costs 1 token, and 1 token = $0.03 for educational in the US, or 0.05€ for commercial in the EU"

"This action costs $0.03 for educational in the US, or 0.05€ for commercial in the EU"

> They can ban trading of credits or let them expire

That sounds extremely user-hostile to me

Isn't this an orthogonal issue that doesn't affect whether billing is done with credits or money?

If the expensive parts of the query happen to work iteratively (especially if agentic), you can act on those loops to bound the cost. Even if it's pure forward generation, you could pause an expensive inference and continue it seamlessly with a cheaper model, adding little to the cost.

You joke but as a parent, I’m so sick of the gem packs, etc. they try to push on the kids to obfuscate your actual spend on games in real world money.

And now it feels like the are gamifying the compute we use for work for all the same reasons.

> effectively $20/$200 in credits for codex

That's not true.

First of all, there's no dollar amount tied to how many credits you get for a subscription.

Second, if you look at the prices for bundles of _extra_ credits and then do some math on the Codex rate card, you'll see that there's no way they would work out to be the same or similar.

> effectively $20/$200 in credits for codex

So, 1.3ish million tokens for Codex? Following the token limit from here https://openai.com/api/pricing/

Based on reading only I think a usable but a step below probably somewhat behind Sonnet. I also did read that some people successfully requested refunds late last year when their models shit the bed due to bugs so if they cut limits hard maybe you can try that

Agreed, I use Z.ai and the usage is fantastic the only temper that recommendation that it's often unreliable. Perhaps a few times per week it's unresponsive. Maybe more often it seems to become flakey.

It's very variable though recently I'm noticing it's more reliable but there was a patch where it was nearly unusable some days.

I guess I won't complain for the price and YMMV.

If you're ok with a model provider that goes down all the time and has such a poor inference engine setup that once you get past 50k tokens you're going to get stuck in endless reasoning loops.

I feel they will go token base at some point, currently if you only use it with precise prompts and not random suggestions, switch between models 5.4 and 5.4 mini depending on the work, it is the best deal.

Yeah, it's really good. Probably going to be the next best deal until they cut back.

I need to try the command line version.

I bought one of the google AI packages that came with a pile of drive storage and Gemini access.

Unfortunately gemini as a coding agent is a steaming useless pile. They have no right selling it, cheap open weight Chinese models are better at this point.

It's not stupid it just is incompetent at tool use and makes bad mistakes. It constantly gets itself into weird dysfunctional loops when doing basic things like editing files.

I'm not sure what GOOG employees are using internally, but I hope they're not being saddled with Gemini 3.1. It's miles behind.

Good luck sticking within limits, I have been burning up my baseline limits insanely fast within a few prompts, a marked change from a few weeks ago.

There's a few complaints online about the same happening to multiple users.

Otherwise anti-gravity has been great.

That's only good for the web based UI. If you want Gemini API access which is what this article is about then you must go the AIStudio route and pricing is API usage based. It does have a free usage tier and new signups can get $300 in free credits for the paid tier so it's I think it's still a good deal, just not as good as using the subscriptions would be.

I'm developing software in this area right now, so I try a lot of the new models. They're not even close for coding tasks. It basically comes down to 26b parameters vs 1T parameters / quantisation / smaller context sizs, there's no comparison. However, for agentic work, tool calling, text summarisation, local LLMs can be quite capable. Workloads that run as background tasks where you're not concerned about TTFB, cold starts, tok/s etc., this is where local AI is useful.

If you have an M processor then I would recommend that you ditch Ollama because it performs slowly. We get double or triple tok/s using omlx or vmlx, respectively, but vmlx doesn't have extensive support for some models like gpt-oss.

I recently experimented creating a Python library from scratch with Codex. After I was done, I took the PRD and Task list that was generated and fed them to opencode with Qwen 3.5 running locally.

Opencode was able to create the library as well. It just took about 2x longer.

Almost as though selling below cost or over capacity will backfire if people find unexpected uses for your product.

Management demands it

I just hit my weekly Max limit 3 days in...

They changed the limits out from under us, and bugs cause usage to spike like crazy.

It’s kind of a rug pull to effectively raise the price like 10x. I can’t afford to finish some of my projects with this change

This is false.

Two examples:

- https://www.msn.com/en-us/money/other/three-years-after-tria...

- https://record.umich.edu/articles/public-school-investment-r...

A lot of HN posters are fighting for their employer and/or investments.

> Every time an Ed Zitron article is posted on HN, it is met with a torrent of vitriol and personal attacks.

It's why I started to pay attention to what he says.

Dude is a bit verbose, but his rationale is solid. If it gets the panties of some people here in a bunch, he may be on to something.

> The articles are okay if not overly wordy

Did you mean instead "The articles are okay if overly wordy"?

I agree with Ed Zitron more often than not, but I do think he Flanderized himself into being the aggressively-anti-AI-guy to the point where he now makes claims about the capabilities of "AI" that are incorrect regularly. I see people on HN doing the same thing, making claims about capabilities that were true as recently as 6 months ago, but aren't true anymore.

[I'm an AI-doomer myself, but I am an AI-doomer because by and large this stuff increasingly works, not because it doesn't.]

That said, Ed Zitron still does a lot of useful research into the economics of the industry and I also believe that continued progress in AI can disrupt the world (for better and for worse) while the economics propping up all the frontier model providers can also implode spectacularly.

Some people talk about how AI doom comes about either way because it could take all of our jobs OR crash the economy when the current bubble bursts. But as an uber-AI-doomer I happen to think there is a very real possibility of a double downside (for the labor class, at least) where both of those things can happen at the same time!

> The articles are okay if not overly wordy but I don’t see how the subject matter elicits that strong of a response.

Hot take, but really it's more of an observation than a take: We saw this exact response in Blockchain & crypto circles a few years ago. (Though HN wasn't quite as culturally "central" to those)

Economic Bubbles are subject to the Tinkerbell Effect. They exist so long as people exist in them, and collapse when either 1) They become so financially unsustainable as to collapse, having consumed all the money the economy could possibly give them, or 2) People stop believing in the bubble and stop feeding it money.

In this regard, the statement "NTFs are stupid" was not merely ridiculing those who bought them, but a direct attack on the bubble and those invested in it. And this is something the people involved in the bubble understand instinctively, even if they aren't consciously aware of it. (There's a psychological mechanism to that, but it's not relevant)

So consequently, they react aggressively to dissent. They seek to enforce their narrative, because not doing so is a threat to the bubble and their financial interests.

---

AI's not much different to that. It's clearly a bubble to everyone including the AI execs saying it out loud.

And people react aggressively to dissent like Ed's, because if the wider public stops believing in AI's future, the bubble bursts. They'll stop tolerating datacenter construction, they'll sell their Nvidia shares, they'll demand regulators restrict AI.

(And to those who can feel their aggression rising reading this comment. Hi, yes. I see you. If I were wrong, nothing I said would matter. You'd be wasting your time engaging with it, history would simply prove me wrong. But by all means, type up that reply or click that button.)

Yeah I've noticed this phenomenon as well. Frankly, I expected to be downvoted into oblivion just for mentioning him. But Zitron's commentary on the financial implications of AI usually reads as common sense to me and checks out (granted I'm not really capable of refuting his points.)

I'd say he's allowed his "mostly correct" opinion on the financial situation to color his "mostly incorrect" opinion on actual AI usefulness.

I wouldn't call it psychosis though. He's committing a natural fallacy where expertise in one area doesn't lend itself to expertise in another.

Those companies at least had somewhat of a moat.

As I see it, the only thing close to a moat is CC for Anthropic, and since it is a big ol' fucking mess that is a) apparently now beyond the ability of any current SOTA LLM to fix, and b) understood by absolutely no human, I'd say it's not much of a moat. The other agents will catch up sooner rather than later.

The other providers? I don't see a moat. We jump ship at the drop of a hat.

yes, but how many succeed without any kind of moat or having destroyed the existing companies?

I'm still running local LLMs and finding perfectly acceptable code gen.

Unfortunately the fools holding the bag are going to be those who own index funds when these companies are inserted into them.

How is it a subpar product? I've been very happy with GPT 5.4 and the Codex CLI tooling, as well as ChatGPT web. I'd say product is one of their strengths.

I'm really curious about how you use it, because for me it was braindead. I tried tasking it to update my personal workout app and it created so many bugs I had to clean up with Opus or be left with spaghetti. It also keeps asking for confirmation of doing basic things.

> I think it signals that they’ve been so successful that they need to ensure there is some direct financial back pressure on heavy users to ensure that their heavy token use is actually economically productive.

Jesus, the spin on this message is making me dizzy.

They finally try to stop running at a loss, and you see that as "they've been so successful"?

Here's how I see it: they all ran out of money trying to build a moat, and now realise that they are commodity sellers. What sort of profit do you think they need to make per token at current usage (which is served at below cost)?

How are they going to get there when less-highly-capitalised providers are already getting popular?

> They seem to have moved beyond lovable and are actually trying to embed some agents in their small businesses, etc.

That's the problem - these small businesses are writign code, models from last year are good enough for them, and as a small business they can easily shell out for hardware to self-host.

The minute businesses take-up AI for their business processed, the will to buy each employee a subscription is going to go the way of the dodo.

Honestly? It was the claude code leak that did it. There was a lot more smoke and mirrors than I anticipated, the poisoning tool calls, how their prompting is, how "messy" a lot of it was etc.

I meant that I thought the exponential with the models is slowing down (AGI, etc). The application though for regular people will continue to go forward.

I've tried z.ai, qwen, deepseek, minmax... they've all been barely half as capable as a middling Codex model.

> I would prefer if it actually explodes sooner rather than later

The idea, as far as I can tell from all the pro-AI developers, was that it will never explode, and the performance will continue increasing so the slop they write today doesn't need maintenance, because when that time comes around there will be smarter models that can clean it up.

If the providers are tightening the screws now (and they are all doing it at the same time), it tells me that either:

1. They are out of runway and need to run inference at a profit.

or

2. They think that this is as good as it is going to get, so the best time to tighten the screws is right now.