OpenAI dropped the price of o3 by 80%

Tangential: please do not use a phone number as a PK. Aside from the nightmare of normalizing them, there is zero guarantee that someone will keep the same number.

OpenAI doesn’t (currently) sell ads. I really cannot see a world where they’re wanting to sell ads to their API users only? It’s not like you need a phone number to use ChatGPT.

To me the obvious example is fraud/abuse protection.

On the flip side it makes a company seem sparklingly inept when they use VOIP as a method to filter valid users. I haven’t done business with companies like Netflix or Uber because I don’t feel like paying AT&T a cut for identity verification. There are plenty of other methods like digital licenses which are both more secure and with better privacy protections.

Maybe they should look into a non-freemium business model. But that won't happen because they want to have as much personal data as possible.

- Parent talks about a paid product. If they wants to burn tokens, they are going to pay for it.

- Those phone requirements do not stop professional abusers, organized crime nor state sponsored groups. Case in point: twitter is overrun by bots, scammers and foreign info-ops swarms.

- Phone requirements might hinder non-professional abusers at best, but we are sidestepping the issue if those corporations deserve that much trust to compel regular users to sell themselves. Maybe the business model just sucks.

Personally I found that rejecting disposable/temporary emails and flagging requests behind VPNs filtered out 99% of abuse on my sites.

No need to ask for a phone or card -- or worse, biometric data! -- which also removes friction.

The discussion wasn't about freemium products though. Someone mentioned that they paid 20 bucks for OpenAI's API already and then they were asked for more verification.

> I'm sure this comment will get downvoted in favor of some other conspiratorial "because they're going to secretly sell my data!" tinfoil post (this is HN of course). But my explanation is the actual reason.

Your explanation is inconsistent with the link in these comments showing Twitter getting fined for doing the opposite.

> Hence why even Facebook requires a unique, non-VOIP phone number to create an account these days.

Facebook is the company most known for disingenuous tracking schemes. They just got caught with their app running a service on localhost to provide tracking IDs to random shady third party websites.

> You can easily block VOIP numbers and ensure the person connected to this number is paying for an actual phone plan, which cuts down dramatically on bogus accounts.

There isn't any such thing as a "VOIP number", all phone numbers are phone numbers. There are only some profiteers claiming they can tell you that in exchange for money. Between MVNOs, small carriers, forwarding services, number portability, data inaccuracy and foreign users, those databases are practically random number generators with massive false positive rates.

Meanwhile major carriers are more than happy to give phone numbers in their ranges to spammers in bulk, to the point that this is now acting as a profit center for the spammers and allowing them to expand their spamming operations because they can get a large number of phone numbers those services claim aren't "VOIP numbers", use them for spamming the services they want to spam, and then sell cheap or ad-supported SMS service at a profit to other spammers or privacy-conscious people who want to sign up for a service they haven't used that number at yet.

Collecting biometrics for training purposes is still collecting biometrics.

If you're worried about the unlimited liability, how about you refund the credits instead of expiring them?

Out of curiosity, what makes you different from a retailer or restaurant that has the same problem?

Why only 365 days? Would be way fairer and still ok for you (if it's such a big issue) to expire them after 5 years.

IIRC, 1 year

Yep, it defaults to doing a web search even when that doesn't make sense.

Example, I asked it to write something. And then I asked it to give me that blob of text in markdown format. So everything it needed was already in the conversation. That took a whole minute of doing web searches and what not.

I actually dislike using o3 for this reason. I keep the default to 4o. But sometimes I forget to switch back and it goes off boiling the oceans to answer a simple question. It's a bit too trigger happy with that. In general all this version and model soup is impossible to figure out for non technical users. And I noticed 4o is now sometimes starting to do the same. I guess, too many users never use the model drop down.

After the last few weeks, where o3 seems desperate to do tool searches or re-crunch a bad gen even though I only asked a question about it, I assumed that the policy is to burn through credits at the fastest possible rate. With this price change, I don't know what's happening now...

IMO its just that the models are very nondeterministic, and people get very different kinds of responses from it. I met a number of people who tried it when it first came out and it was just useless so they stopped trying it, other people (including me) got gobsmacking great responses and it felt like AGI was around the corner, but after enough coin flips your luck runs out and you get some lazy responses. Some people have more luck than others and wonder why everyone around them says it's trash.

> 3 out of 4 companies I consulted for that started using AI for coding marked cost as an important criteria.

And those aren't average customers.

I break out Gemini 2.5 pro when Claude gets stuck, it's just so slow and verbose. Claude follows instructions better and seems to better understand it's role in agentic workflows. Gemini does something different with the context, it has a deeper understanding of the control flow and can uncover edge case bugs that Claude misses. o3 seems better at high level thinking and planning, questioning if it should it be done and whether the challenge actually matches the need. They're kind of like colleagues with unique strengths. o3 does well with a lot of things, I just haven't used it as much because of the cost. Will probably use it more now.

I don't think it's simply a stylistic matter: it seems reasonable to assume that text in books tends to have higher information density, and contains longer and more complicated arguments (when compared to text obtained from social media posts, blogs, shorter articles, etc). If you want models that appear more intelligent, I think you need them to train on this kind of high-quality content.

The fact that these tend to be written in an older writing style is to me incidental. You could rewrite all your college text books in contemporary social media slang and I would still consider them high-quality texts.

Anecdotal, but about a week ago I noticed a sharp drop in o3 performance. For many tasks I will compare Gemini 2.5 Pro with o3, running the same prompt in both. Generally for my personal use o3 and G2.5P have been neck-and neck over the last months, with responses I have been very happy with.

However starting from a week ago, the o3 responses became noticeably worse, with G2.5P staying about the same (in terms of what I've come to expect from the two models).

This alongside the news that you guys have decreased the price of o3 by 80% does really make it feel like you've quantized the model or knee-capped thinking or something. If you say it is wholly unchanged I'll believe you, but not sure how else to explain the (admittedly subjective) performance drop I've experienced.

Unrelated: Can you all come up with a better naming scheme for your models? I feel like this is a huge UX miss.

o4-mini-high o4-mini o3 o3-pro gpt-4o

Oy.

Is it o3 (low), o3 (medium) or o3 (high)? Different model names have crept into the various benchmarks over the last few months.

Just because you work at openAI doesn't mean you know everything about openAI especially as strategic as nerfing models to save costs

What's with the dropped benchmark performance compared to the original o3 release? It was disappointing to not see o4-mini on it as well

Not quantized?

I think the parent-parent poster has explained why we can't trust you (and work on OpenAI doesn't help they way you think it does).

I didn't read the ToS, like everyone else, but my guess is that degrading model performance at peak times will be one of the things that can slip through. We are not suggesting you are running a different model but that you are quantizing it so that you can support more people.

This can't happen with Open weight models where you put the model, allocate the memory and run the thing. With OpenAI/Claude, we don't know the model running, how large it is, what it is running on, etc... None of that is provided and there is only one reason that I can think of: to be able to reduce resources unnoticed.

I think Gell-Mann amnesia happens here too, where you can see how wrong HN comments are on a topic you know deeply, but then forget about that when reading the comments on another topic.

how do you sample it behind the scenes? usually best of X means you generate X outputs and you choose best result.

if you could do this automatically, it would be game changer as you could run top 5 best models in parallel and select best answer every time

but it's not practical because you are the bottleneck as you have to read all 5 solutions and compare them

I used to think the models got worse over time as well but then I checked my chat history and what I noticed isn't that ChatGPT gets worse, it's that my standards and expectations increase over time.

When a new model comes out I test the waters a bit with some more ambitious queries and get impressed when it can handle them reasonably well. Over time I take it for granted and then just expect it to be able to handle ever more complex queries and get dissappointed when I hit a new limit.

there's definitely measurements (eg https://hdsr.mitpress.mit.edu/pub/y95zitmz/release/2 ) but I imagine they're rare because those benchmarks are expensive, so nobody keeps running them all the time?

Anecdotally, it's quite clear that some models are throttled during the day (eg Claude sometimes falls back to "concise mode" - with and without a warning on the app).

You can tell if you're using Windsurf/Cursor too - there are times of the day where the models constantly fail to do tool calling, and other times they "just work" (for the same query).

Finally, there's cases where it was confirmed by the company, like Gpt-4o's sycopanth tirade that very clearly impacted its output (https://openai.com/index/sycophancy-in-gpt-4o/)

I assumed it was because the first week revealed a ton of safety issues that they then "patched" by adjusting the system prompt, and thus using up more inference tokens on things other than the user's request.

My suspicion is it's the personalization. Most people have things like 'memory' on, and as the models increasingly personalize towards you, that personalization is hurting quality rather than helping it.

Which is why the base model wouldn't necessarily show differences when you benchmarked them.

I think it's an illusion. People have been claiming it since the GPT-4 days, but nobody's ever posted any good evidence to the "model-changes" channel in Anthropic's Discord. It's probably just nostalgia.

Yeah, it’s almost certainly hallucination (by the human user).

It's probably less often quantizing and more often adding more and more to their hidden system prompt to address various issues and "issues", and as we all know, adding more context sometimes has a negative effect.

Gemini is simply that good. I’m trying out Claude 4 every now and then and go back to Gemini to fix its mess…

When you say "jammed," how do you mean?

Are you able to quantify how quickly your perception gets skewed by how long you use the models?

It's still a very competitive marketplace

honestly refreshing take.

But OpenAI breathes honesty. They're open source! They would never do such a thing. /s

Or they are using a speedy add-on decoder.

Gemini is objectively exhibiting new behavior with the same prompts and that behavior is unwelcome. It includes hallucinating information and refusing to believe it's wrong.

My question is not whether this is true (it is) but why it's happening.

I am willing to believe the aider community has found that Gemini has maintained approximately equivalent performance on fixed benchmarks. That's reasonable considering they probably use a/b testing on benchmarks to tell them whether training or architectural changes need to be reverted.

But all versions of aider I've tested, including the most recent one, don't handle Gemini correctly so I'm skeptical that they're the state of the art with respect to bench-marking Gemini.

https://aider.chat/docs/leaderboards/by-release-date.html

Updated yesterday... https://aider.chat/docs/leaderboards/

I'm an average Ivan, and I got access.

Yeah, I just don't see myself using o3 when I have Gemini-2.5 Pro. I don't recall if Google Cloud verified my ID in the past, though. Still, no need to let yet another organization have my data if I'm not getting something better in return.

It's most likely for regulation compliance, instead of a sincere attempt to block anyone from training on them.

Agreed 100% o3 is great but the rate limit window and the quota itself both render it almost useless for more than one off fixes.

It's great with those, however!

You trust their PR statements?

> "engineers optimizing inferencing"

They finally implemented DeepSeek open source methods for fast inference?

They increased prices and are not selling a pure commodity tho

We don't even know yet if the model is the product though, and if OpenAI is the company that will make the AI product/model, (chat that keeps expanding into other functionalities and capabilities) or will it be 10,000 companies using the OpenAI models. (well, it's probably both, but in what proportion of revenue)

Unlike Uber or whatsapp, there's no network effect. Don't think this is a winner takes all market, there was an article where we had this discussion earlier. Players who get a small market share are immediately profitable proportional to the market share (given a minimum size is exceeded.)

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It wouldn't surprise me if they try, but ironically if GPT is a good personal shopper, it might make it harder to monetize with ads because people will trust the bot's organic responses more than the ads.

You could override its suggestions with paid ones, or nerf the bot's shopping abilities so it doesn't overshadow the sponsors, but that will destroy trust in the product in a very competitive industry.

You could put user-targeted ads on the site not necessarily related to the current query, like ads you would see on Facebook, but if the bot is really such a good personal shopper, people are literally at a ChatGPT prompt when they see the ads and will use it to comparison shop.

Probably a bad example from my part, but also because of increasing the costs and offering a tier with ads. I was mostly talking about the Netflix as it was originally concieved. "Give access to unlimited content at a flat fee", which didnt scale pretty well.

In addition to what the parent commenter was likely referring to, there are also the Zizians: https://en.wikipedia.org/wiki/Zizians

Scroll through the ChatGPT subreddit right now and tell me there isn't a TON of people in there who are legitimately unwell. Reads like the back page notes of a dystopian novel.

https://futurism.com/chatgpt-mental-health-crises, which references the more famous https://www.rollingstone.com/culture/culture-features/ai-spi... but is a newer article.