GPT-4.5: "Not a frontier model"?

Rumors said that GPT4.5 is an order of magnitude larger. Around 12 trillion parameters total (compared to GPT4's 1.2 trillion). It's almost certainly MoE as well, just a scaled up version. That would explain the cost. OpenAI also said that this is what they originally developed as "Omni" - the model supposed to succeed GPT4 but which fell behind expectations. So they renamed it 4.5 and shoehorned it in to remain in the news among all those competitor releases.

>The price is a bit of a mystery

I think it at least is somewhat analogous to what happened with pricing on previous models. GPT 4, despite being less capable than 4o, is an order of magnitude more expensive, and comparably expensive to o1. It seems like once the model is out, the price is the price, and the performance gains emerge but they emerge attached to new minified variations of previous models.

I don't think th October 2023 training cut-off means the model finished pre-training a year ago. All of OpenAI's models share that same cut-off date.

One theory is that they're worried about the increasing tide of LLM-generated slop that's been posted online since that date. I don't know if I buy that or not - other model providers (such as Anthropic, Gemini) don't seem worried about that.

Releasing it was probably a mistake. In context what the model is could have been understood, but they haven’t really presented that context. Also it would be lost on a general audience.

The general public will naturally expect it to be the next big thing. Wasn’t that the point of releasing it? To seem like progress is being made? To try to make that point with a model that doesn’t deliver is a misstep.

If I were Sam Altman, I’d be pulling this back before it goes on general release, saying something like it was experimental and after user feedback the costs weren’t worth it and they’re working on something else as a replacement. Then o3 or whatever they actually are working on instead can be the “replacement” even if it’s much later.

I sort of believed this but also 4.5 coming out last year would absolutely have been a big deal compared to what was out there at the time? I just dont understand why they would not launch it then.

> slew of strong non-reasoning models (Gemini 2.0 Flash, Grok 3, Sonnet 3.7)

Sonnet 3.7 is actually reasoning model.

Even this is a bit overly complicated/optimistic to me. Why not something as simple as: OpenAI has been building larger and larger models to great success for a long time. As a result, they were excited this one was going to be so much larger=so much better that the price to run it would be well worth the huge jump they were planning to get from it. What really happened is this method of scaling hit a wall and they were left with an expensive dud they won't get much out of but they have to release something for now otherwise they start falling well behind on the boards the next few months. Meanwhile they scramble focus to find other means of scaling like "chain of thought + runtime" provided.

Time to enter the tick cycle.

I ask chatgpt to give me a map highlighting all spanish speaking countries, gives me stable diffusion trash.

Just gotta do the grunt work, add a tool with a map api. Integrate with google maps for transit stuff.

It's a good LLM model already it doesn't need to be einstein and solve aerospatial equations. We just need to wait until they realize their limits and find the humility to build yet another useful product that won't conquer the world.

They should have called it "ChatGPT Enterprise".

Apparently, OpenAI API “credits” expire after a year. I stupidly put another $20 and trying to blow through them, 4.5 is the easiest way considering recent 4o has fallen out of favor for other models and I don’t want to just let them expire again. An expiry after only one year is asinine.

This is how pricing on human labour works. Nobody expects an employee that costs twice as much to produce twice the output for any given task. All that is expected is that they can do a narrow set of things, that another person can't.

The 4.5 has better 'vibes' but isn't 'better', as a concrete example:

> Mission is the operationalized version of vision; it translates aspiration into clear, achievable action.

The "Mission is the operationalized version of vision" is not in the corpus that I am find and is obviously a confabulated mixture of classic Taylorist like "strategic planning"

SOPs and metrics, which will be tied to compensation and the unfortunate ubiquitous nature of Taylorism would not result in shared purpose, but a bunch of Gantt charts past the planning horizon.

IMHO I would consider "complex nuanced thought" as understanding the historical issues and at least respect the divide between classical and neo-classical org theory. Or at least avoid pollution of more modern theories with classical baggage that is a significant barrier to delivering value.

Mission statements need to share strategic intent in an actionable way, strategy is not operationalization.

I believe 4.5 is a very large and rich model. The price is high because it's costly to inference; however, the bigger reason is to ensure that others don't distill from it. Big models have a rich latent space, but it takes time to squeeze the juice out.

So this kind of mirrors my feelings after using GPT-4.5 on general conversation and song writing.

GPT picked up on unspecified requirements almost instantly. It is subtle (and may be undesirable in some contexts). For example in my songs, I have to bracket the section headings, it picked up on that from my original input. All the other frontier models generally have to be reminded. Additionally, I separately asked for an edit to a music style description. When I asked GPT-4.5 to write a song all by itself, it included a music style description. No other model I have worked with has done this.

These are subtle differences, but in aggregate the model just generally needs less nudging to create what is required.

Sesame model for voice audio imo is better than ChatGPT voice audio conversation. They are going to open source it as well.

We were using gpt-4o for our chat agent, and after some experiments I think we'll move to flash 2.0. Faster, cheaper and a bit more reliable even. I also experimented with the experimental thinking version, and there a single node architecture seemed to work well enough (instead of multiple specialised sub agents nodes). It did better than deepseek actually. Now I'm waiting for the official release before spending more time on it.

For the rest of us using free tiers ChatGPT is hands down the winner allowing limited image generation, unlimited usage of some model and limited usage of 4o.

Claude is still stuck at 10 messages per day and gemini is less accurate/useful.

I think it should be illegal to trick humans into reading "creative" machine output.

It strikes me as a form of fraud that steals my most precious resources: time and attention. I read creative writing to feel a human connection to the author. If the author is a machine and this is not disclosed, that's a huge lie.

It should be required that publishers label AI generated content.

Consumer limits. When something is good enough it can make stable money, then there is no real incentive to innovate beyond the bare minimum—just enough to keep consumers engaged, shareholders satisfied, and regulators at bay.

This is how modern capitalism and all corporations work, we will keep receiving new numbers in versions without any sensible change – consumers will keep updating their subscriptions out of habit – xyzAI PR managers, HR managers, corporate lawyers and myriads of other bureaucrats will keep receiving their paychecks secretly dreaming of retirement, xyzAI top management will burn money on countless acquisitions just to fight boredom, turning into xyz(ai)MEGAcorp doing everything from crude oil processing and styrofoam cups to web services and AI models.

No modern mega corporation is capable of making something else or different from what already worked for them just once. We could achieve universal wellfare and prosperity 60 years ago, that would’ve disrupted the cycle. Instead, we got planned obsolescence, endless subscription models, and a world where everything “new” is just a slightly repackaged version of last year’s product.

Yes, I believe the sprint is over, now its doing to be slow cycles maybe 18 months to see a 5% increase an ability and even that 5% increase will be highly subjective. Claude's new release is about the same 3.7 is arguably worse at some things than 3.5 and better at others. Based on the previous pace of release in about 6 months or so - if the next release from any of the leaders is about the same "kinda better kinda worse" then we'll know. Imagine how much money is going to evaporate from the stock market if this is the limit!!!

I also hate waiting on reasoning.

I much would prefer a super lightning fast model that is cheaper but the same quality as these frontier models.

Let me query these things to death.

does it mean we get a reprieve from "this is just the beginning" comments.

No, it means that it got better on things orthogonal to what we have mostly been measuring. On the last few rounds, we have been mostly focusing on reasoning, not as much on knowledge, "creativity", or emotional resonance.

Only an older version of GPT-4o has been deprecated and will be removed in May. The newest version will be supported through at least 20 November 2025.

https://learn.microsoft.com/en-us/azure/ai-services/openai/c...

I think this framing isn't quite right either. DeepSeek's R1 isn't very different from what OpenAI has already been doing with o1 (and that other groups have been doing as well). As for distilling - the R1 "distilled" models they released aren't even proper (logit) distillations, but just SFTs, not fundamentally new at all. But it's great that they published their full recipes and it's also great to see that it's effective. In fact we've seen now with LIMO, s1/s1.1, that even as few as 1K reasoning traces can get most LLMs to near SOTA math benchmarks. This mirrors the "Alpaca" moment in a lot of ways (and you could even directly mirror say LIMO w/ LIMA).

I think the main takeaway of GPT4.5 (Orion) is that it basically gives a perspective to all the "hit a wall" talk from the end of last year. Here we have a model that has been trained on by many accounts 10-100X the compute of GPT4, is likely several times larger in parameter count, but is only... subtly better, certainly not super-intelligent. I've been playing around w/ it a lot the past few days, both with several million tokens worth of non-standard benchmarks and talking to it and it is better than previous GPTs (in particular, it makes a big jump in humor), but I think it's clear that the "easy" gains in the near future are going to be figuring out how as many domains as possible can be approximately verified/RL'd.

As for the release? I suppose they could just have kept it internally for distillation/knowledge transfer, so I'm actually happy that they released it, even if it ends up not being a really "useful" model.