Has OpenAI Lost Its Way?

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Has OpenAI Lost its Way?

Since the release of GPT-4.5 on Thursday, the industry has been filled with negativity toward OpenAI, with claims that it has lost its lead or is out of touch with prices.

But despite being someone who isn’t particularly fond of OpenAI, I believe all these claims are exaggerated, and the conclusions that people have reached come from the fact that they are evaluating the model in the utterly wrong fashion.

To me, this appears to be a clear decoy by OpenAI ahead of a more significant release, GPT-5. They are purposefully downplaying this release, and in fact, the prices they’ve set are high on purpose; they DON’T want you to use the model.

But why?

Today, we will describe all the details, clarifying the apparent confusion around the model. You will also learn how GPT-4.5 is the precursor to two new models (merged into one, the ‘distilled reasoner’), laying out OpenAI’s grand plan. Finally, we will discuss what this release actually means from the perspective of the entire industry, which is the real takeaway.

Let’s dive in.

An Apparently Disastrous Release

When even OpenAI themselves acknowledged that GPT-4.5 ‘was not frontier’, during the live stream, and instead focused on the idea that the real improvement comes at the level of knowledge (knows a lot more about stuff, less hallucination prone) and that it captures user intent better, we knew the release of GPT-4.5 was not going to be a GPT-4-level type of release, despite being the largest model ever trained.

An Unamusing Improvement

The famous AI lab was surprisingly short of performance graph presentations, presenting just three:

In the first two, they showed how GPT-4.5 knew more about the world according to their knowledge-testing SimpleQA benchmark and also showed decreased hallucination rates. GPT-4.5 is a more knowledgeable model with firmer conviction in the things it says.

Despite always representing hallucinations in a bad light, they are actually a feature of these models. They are, in a way, introduced on purpose to foster more model creativity by sampling a random word out of the ‘x most likely ones.’ No, LLMs rarely give you the most likely word (‘Engineer’ in the image below) and instead give one of the top most likely (‘Lawyer’ or ‘Doctor’ are also semantically viable so that they can get chosen, too).

This means the hallucination problem is a two-headed beast that depends not only on how certain the model is about its predictions but also how to balance it with the need for creativity.

In the third graph (image below), they compared the model to GPT-4o in a vibe-check…

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