Is this not just about extra credit? So what's included in the subscription doesn't change - just extra credits are now token based instead of message based? (For Plus/Pro)
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.
Why not just attach a real dollar amount, rather than using "credits"?
Well, I know why. I just wanted to be snarky. It's just that trying to hide the actual price is getting a bit old. Just tell me that generating this much code will cost me $10.
I hate that pattern so much. It’s also not just to obfuscate the spending - it’s also to ensure you already have some amount left over in your account, so that it feels like you’re not spending as much to just “top up” and afford that one thing you want this time.
If you have some left over that you can’t spend, it feels like you’ve “wasted” them.
The answer is so that they can charge different prices per credit. If you buy low amounts, they can charge one price. If you buy in bulk, they can offer a discount. The usage is the same, but they can differentiate price per usage to give people more a favorable price if they are better customers.
The title is misleading and not in the article. This change is for business/enterprise accounts. Also, these are still credit based. The change is that credits now operate on tokens like the API rather than on messages as they used to.
> Customers on existing Plus, Pro and Enterprise/Edu plans should continue to use the legacy rate card. We’ll migrate you to the new rates in the upcoming weeks.
Nope, they buried the lead a bit but this is coming for _all_ users, even pro/plus subscription plans. So you get chatgpt pro/plus benefits, and then effectively $20/$200 in credits for codex
Check out z.ai coder plan. The $27/mo plan is roughly the same usage as the 20x $200 Claude plan. I have both and Claude is a little better, but GLM 5.1 is much better value.
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.
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.
Agreed. They had a rough patch around the 4.7 to 5 upgrade. New architecture required hardware migration. The 5 to 5.1 upgrade was much smoother (same architecture new weights). As you say, little rough around edges, but still great value. Trick I learned is that it's max 2 parallel requests per user. You can put a billion tokens a month through it, but need to manage your parallelism.
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.
No? Isn't the article about Codex, which is roughly equivalent to "Gemini CLI" and Google's Antigravity? Google's subscriptions include quotas for both of those, albeit the $20 monthly "Pro" plan has had its "Pro" model quota slashed in the last few weeks. You still get a large number of "Gemini 3 Flash" queries, which has been good enough for the projects I've toyed with in Antigravity.
I guess that's true but I find Google's models better than their public tooling. The Pro subscription includes "Gemini Code Assist and Gemini CLI" but the Gemini Code Assist plugin for IntelliJ which is my daily driver is broken most of the time to the degree that it's completely unusable. Sometimes you can't even type in the input box.
The only way I can do serious development with Gemini models is with other tooling (Cline, etc) that requires API based access which isn't available as part of the subscription.
If you aggressively use all buckets Google is incredibly generous. In theory for one AI pro subscription you can get what is a ridiculous return in investment in a family plan.
You could probably be charging google literally thousands if all 6 members were spamming video and image generation and antigravity.
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.
We are exiting a hype cycle, well into the adoption curve. Subscriptions were never going to last.
My next step is going to be evaluating open and local models to see if they are sufficiently close to par with frontier models.
My hope is that the end of seat based pricing comes with this tech cycle. I was looking for document signing provider that doesn't charge a monthly, I only need a few docs a year.
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.
Kimi K2.5 (as an example) is an open model with 1T params. I don't see a reason it has to be local for most use cases- the fact that it's open is what's important.
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.
So Anthropic bundled CC with Claude.ai cuz OAI bundled chatgpt with Codex, now OAI is unbundling, IPO must be around the corner. Writing is also on the wall for CC usage based subscriptions now that main competitor effectively got rid of it. How are the Chinese models looking?
Is this something that is likely to also change the way Github Copilot bills? Right now the billing is message-based, not token-based. And OpenAI and Microsoft are rather opaquely intertwined in the AI space.
Hard to say, but GitHub Copilot also allows access to Anthropic, Google and Grok models, so I don't know that a change from a single provider would necessarily change how they bill
Although I have to say I am sometimes surprised how much people burn through their usage. I was briefly on a Claude Max plan and then switched to a pro plan and still almost never hit my limit.
It's really not. As a one-person IT department I'm now able to build things in hours or days that it previously would have taken my weeks or even months to build (and thus they didn't get done). Things people have wanted for years that I didn't ever have the time for, I can now say "yes" to.
Absolutely not. I took on some thins that would normally take 5-10 people and many months.
Some people are turn out slop. I was really excited to try and make some impressive shit. My whole life has been dedicated to trying to embody what Apple preached in the early days.
I knew this was coming, but I thought I had a little more time to try and get them over the finish line, ya know?
Maintenance by hand might be achievable, but it’s extremely hard when you’ve built something really big.
I’ve only got so much savings left to live on.
I’m not saying anyone owes me anything, but we all need to pivot and in a lot less sure my pivot is going to work out now
> I took on some thins that would normally take 5-10 people and many months.
Based on what, exactly?
It's very easy to claim some software would've taken you months to make, but this is ridiculous. Estimating project duration is well known to be impossible in this field. A few years ago you'd get laughed out the room for making such predictions.
> I’ve only got so much savings left to live on.
Respectfully, what are you doing here?
Yeah sure, the Apple dream. But supposing AI did in fact make you this legendary 100x developer, so it would to everyone else including those with significantly more resources. You'd still be run out of the market by those with bigger budgets or more marketing, and end up penniless all the same.
I would strongly recommend you not put all your proverbial eggs in this basket.
If my math is right, assuming a mix of around 70% cached tokens, 20% input tokens, and 10% output tokens, it breaks even with the old pricing at around 130k tokens per message, or about 13k output tokens per message.
With the hidden reasoning tokens and tool calls, I have no idea how many tokens I typically use per message. I would guess maybe a quarter of that, which would make the new pricing cheaper.
Ultimately, we need to know the true cost of this technology to evaluate how effectively or ineffectively it can displace the workforce that existed before it.
MiniMax M2.7, MiMo-V2-Pro, GLM-5, GLM5-turbo, Kimi K2.5, DeepSeek V3.2, Step 3.5 Flash (this last one is particularly cheap while still being powerful).
Every time an Ed Zitron article is posted on HN, it is met with a torrent of vitriol and personal attacks. The articles are okay if not overly wordy but I don’t see how the subject matter elicits that strong of a response.
At any rate, this observation is not unique to Ed, lots of people have made the same conclusion that the math doesn’t add up from a business profitability perspective.
> 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.)
Literally every VC funded consumer product has switched from a "growth at all costs" phase to a "Now we hike prices, make money, and generally enshittify" phase, and tons of those companies are still around (e.g. Uber), so I'm not sure why anyone thinks it would be much different for AI.
I think the situation we'll end up in is having closed models that are fast and near perfect but expensive, and a lot of cheap open-source models that are good enough for most people.
Billions of USD in debt, a business model bleeding cash with no profit in perspective, high-competition environnement, a sub-par product, free-to-use offline models taking off, potential regulatory issues, some investor commitments pulling out... tricky.
But let's not cry for the founders, they managed to get away with tons of money. The problem is for the fools holding the bag.
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.
5.4 is great. I use it for python professionally and for typescript/front-end games and educational apps recreationally. In my experience it's roughly as good as opus, just a lot cheaper. It's amazing how much usage you get for $20/mo
This pricing only really makes sense if the users can predict their usage, if not people that use this heavily are just going to be hamstrung and are going to start rationing their usage.
from what they wrote, they're just changing how they measure the usage; might even be a good thing if you manage your context right:
> This format replaces average per-message estimates for your plan with a direct mapping between token usage and credits. It is most useful when you want a clearer view of how input, cached input, and output affect credit consumption.
The current pricing model (for plus) feels deliberately confusing to me, I can never really tell if I'm nearing any kind of limit with my account since nothing really seems to tell me.
If you use Google's tooling but not if you need API access. API access is not in the subscriptions and uses token based pricing. For development I find that the Gemini IDE plugins that have good free usage and are included in the subscriptions aren't great. Gemini plug-in under IntelliJ is often broken, etc. The best experience is with other tools like Cline where you've had to use a developer based account which is API usage based already.
But Gemini's API based usage also has a free tier and if that doesn't work for you (they train on your data) and you've never signed up before you get several hundred dollars in free credits that expire after 90 days. 3 months of free access is a pretty good deal.
Does this mean there’s no such thing as a “subscription” to ChatGPT for businesses? I thought they offered businesses a subscription with some amount of built in quota previously, including for the side products like codex and sora.
There are still subscriptions that give access to both ChatGPT and Codex, but with a much smaller usage quota than before the change (which came at the same time as the end of the 2x promo). I couldn't find the equivalent in terms of credit for the usage included with these $20/25 seats...
> This format replaces average per-message estimates with a direct mapping between token usage and credits.
It's to replace the opaque, per-message calculation, not the subscription plan.
Well, I know why. I just wanted to be snarky. It's just that trying to hide the actual price is getting a bit old. Just tell me that generating this much code will cost me $10.
- 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)
- They can ban trading of credits or let them expire
Even for a single standalone LLM that's the case, and the 'agentic' layers thrown on top just make that problem exponentially worse.
One'd need to entirely switch away from LLMs to fix this problem.
And now it feels like the are gamifying the compute we use for work for all the same reasons.
If you have some left over that you can’t spend, it feels like you’ve “wasted” them.
The answer is so that they can charge different prices per credit. If you buy low amounts, they can charge one price. If you buy in bulk, they can offer a discount. The usage is the same, but they can differentiate price per usage to give people more a favorable price if they are better customers.
Is there anything wrong with that?
Now I'm going to have to find the new best deal.
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.
The only way I can do serious development with Gemini models is with other tooling (Cline, etc) that requires API based access which isn't available as part of the subscription.
You could probably be charging google literally thousands if all 6 members were spamming video and image generation and antigravity.
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.
There's a few complaints online about the same happening to multiple users.
Otherwise anti-gravity has been great.
My next step is going to be evaluating open and local models to see if they are sufficiently close to par with frontier models.
My hope is that the end of seat based pricing comes with this tech cycle. I was looking for document signing provider that doesn't charge a monthly, I only need a few docs a year.
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.
Opencode was able to create the library as well. It just took about 2x longer.
And I just subscribed for a year's worth of Claude... Terrible timing I guess. Do you know if the open models are viable?
That said: competition will soon kick in.
Some people are turn out slop. I was really excited to try and make some impressive shit. My whole life has been dedicated to trying to embody what Apple preached in the early days.
I knew this was coming, but I thought I had a little more time to try and get them over the finish line, ya know?
Maintenance by hand might be achievable, but it’s extremely hard when you’ve built something really big.
I’ve only got so much savings left to live on.
I’m not saying anyone owes me anything, but we all need to pivot and in a lot less sure my pivot is going to work out now
Based on what, exactly?
It's very easy to claim some software would've taken you months to make, but this is ridiculous. Estimating project duration is well known to be impossible in this field. A few years ago you'd get laughed out the room for making such predictions.
> I’ve only got so much savings left to live on.
Respectfully, what are you doing here?
Yeah sure, the Apple dream. But supposing AI did in fact make you this legendary 100x developer, so it would to everyone else including those with significantly more resources. You'd still be run out of the market by those with bigger budgets or more marketing, and end up penniless all the same.
I would strongly recommend you not put all your proverbial eggs in this basket.
/s
With the hidden reasoning tokens and tool calls, I have no idea how many tokens I typically use per message. I would guess maybe a quarter of that, which would make the new pricing cheaper.
Ultimately, we need to know the true cost of this technology to evaluate how effectively or ineffectively it can displace the workforce that existed before it.
The infrastructure build out just can't keep up with it.
Two examples:
- https://www.msn.com/en-us/money/other/three-years-after-tria...
- https://record.umich.edu/articles/public-school-investment-r...
If I recall correctly, Ed Zitron noted in a recent article that one of the horsemen of his AI-pocalypse would be price hikes from providers.
At any rate, this observation is not unique to Ed, lots of people have made the same conclusion that the math doesn’t add up from a business profitability perspective.
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.)
I'm still running local LLMs and finding perfectly acceptable code gen.
But let's not cry for the founders, they managed to get away with tons of money. The problem is for the fools holding the bag.
Gemini burned me too many times but maybe the situation has improved since.
> This format replaces average per-message estimates for your plan with a direct mapping between token usage and credits. It is most useful when you want a clearer view of how input, cached input, and output affect credit consumption.
But Gemini's API based usage also has a free tier and if that doesn't work for you (they train on your data) and you've never signed up before you get several hundred dollars in free credits that expire after 90 days. 3 months of free access is a pretty good deal.