GPT-4.5 is the end of an era, politely

OpenAI released GPT-4.5 as a research preview this week. It is their largest model, it is not a reasoning model, and its API pricing is roughly an order of magnitude above GPT-4o.

The company's own framing is unusually candid: this is the last of the non-chain-of-thought line, it is better at the things large pretrained models are better at — world knowledge, writing quality, fewer hallucinations, something they describe as improved "EQ" — and it is not designed to compete on math and coding benchmarks against reasoning models.

That framing is correct and it is also an obituary.

what the scaling curve says now#

For most of 2020 to 2023, the answer to "how do we get a better model" was "make it bigger." Scaling laws were the field's organizing principle. GPT-4.5 is what you get when you keep pulling that lever with 2024-era techniques, and what you get is: meaningfully better at some qualitative things, not competitive on the benchmarks people actually optimize for, and expensive enough that the economics are hostile.

Meanwhile, test-time compute — spending inference tokens on reasoning — produces larger gains on hard problems for far less capital. The lever moved.

This does not mean pretraining scale is dead. Reasoning models are built on pretrained base models and a better base makes a better reasoner. It means the marginal dollar goes to post-training and inference compute rather than to another pretraining order of magnitude.

where the big model is actually better#

Worth being fair to it, because the discourse is going to flatten this into "GPT-4.5 flopped."

Large non-reasoning models are genuinely better at:

• Writing that sounds like a person. Reasoning models often produce prose that reads like a report. This one does not.
• Broad factual recall. More parameters means more memorized world.
• Fewer confident fabrications on knowledge questions, per OpenAI's own hallucination evaluations.
• Following a conversation with implicit context over many turns.

If your product is a writing tool, a support agent, or anything where tone and recall matter more than multi-step logic, a bigger base model is the right choice and always was.

the pricing problem#

The price is the story for most developers. At those rates, the set of applications where GPT-4.5 is the correct economic choice is small. It is a model for cases where quality dominates cost, which is a real category and a narrow one.

Expect this to be the pattern going forward: a small number of very expensive models for the top of the quality curve, a large middle of cost-effective workhorses, and cheap small models absorbing everything routine. Route accordingly, and instrument your routing so you know what you are actually spending per feature.

the historical note#

Someone will write a retrospective in a few years that treats February 2025 as the moment the pure-scale thesis visibly stopped being the main event. They will be oversimplifying, because these transitions are always gradual and the narrative is always cleaner in hindsight.

But they will not be wrong about the direction.