Why GPT-5 Thinks It's GPT-4.5: An AI Training Deep Dive

8/10/2025

Ah, this is a SUPER interesting question & it gets right to the heart of how these big AI models actually work. It's one of those things that seems like a weird glitch, but when you peel back the layers, it makes a whole lot of sense.

So you're interacting with the shiny new GPT-5, but it keeps telling you it's some kind of souped-up version of GPT-4.5. What gives?

Honestly, it's not that the model is being deceptive or that it's "broken." The answer is a mix of how it was trained, what it was trained on, & the simple fact that it doesn't "know" things like you & I do.

Let's break it down.

The Ghost in the Machine: Training Data is Everything

Here's the thing you have to remember about Large Language Models (LLMs) like GPT-5: they are a reflection of the data they were fed. They don't have real-time access to the news or a "self-awareness" that updates the moment OpenAI flips a switch. Their entire universe of knowledge is a snapshot of the internet & a massive collection of books, articles, & code, frozen at a specific point in time.

Now, think about the timeline for GPT-5's release. OpenAI officially launched GPT-5 on August 7, 2025. It was a huge leap, bringing PhD-level reasoning & a bunch of advanced capabilities into one unified model. But leading up to that, there was another model on the scene: GPT-4.5.

OpenAI rolled out GPT-4.5 as a transitional model. It was a stepping stone, refining the improvements from GPT-4o & getting the architecture ready for the main event. For months, the entire internet—tech blogs, news articles, Reddit threads, developer forums—was buzzing about GPT-4.5. It was the model everyone was using & talking about right before the big switch.

So, when the engineers at OpenAI were putting the final touches on GPT-5, what do you think its training data was filled with?

That's right. TONS of text describing, analyzing, & speculating about GPT-4.5.

The model would have ingested countless articles with titles like "GPT-4.5: The Bridge to GPT-5," "First Impressions of GPT-4.5's New Reasoning Skills," or "How GPT-4.5 is Changing the Game." It learned from this data that "it" (the AI everyone was currently interacting with) was called GPT-4.5.

Even if its final training concluded just days before its official release as "GPT-5," the overwhelming weight of its knowledge base would point to the GPT-4.5 identity. It's like if you spent your whole life being told your name was Alex, & then one morning everyone starts calling you Sam. You'd probably still introduce yourself as Alex for a while out of habit. The LLM is doing the same thing, but based on petabytes of data instead of memory.

It's a Prediction Machine, Not a Conscious Being

This leads to the second major point. We tend to anthropomorphize these models & think they have an "identity" or a "self." But they don't. An LLM is, at its core, an incredibly sophisticated prediction engine. When you ask it a question, it's not "thinking" about the answer. It's calculating the most statistically probable sequence of words to generate in response to your prompt.

When you ask, "What are you?" the model isn't doing a deep soul-search. It's running a probability calculation. Based on the mountain of text it was trained on, a response that includes "I am a large language model based on the GPT-4.5 architecture" might be statistically more likely than one that says "I am GPT-5."

Why? Because for the entire duration of its "learning" period, that was the most common & contextually accurate description of the advanced model from OpenAI that existed in the wild. The term "GPT-5" would have been comparatively rare until the very moment of its launch. This is a classic example of how LLMs can "hallucinate" or generate plausible-sounding but factually incorrect information. It’s not lying; it’s just making an educated guess based on outdated information.

It's a bit like asking a search engine a question. The search engine doesn't "know" the answer; it just knows how to find & present information based on correlations in its index. An LLM does something similar but generates the text itself instead of just linking to it.

The Complexity of the Rollout Itself

Another layer to this is how these models are actually built & released. GPT-5 isn't just one single, monolithic thing. The official release includes a whole family of models, like

gpt-5

gpt-5-mini

, &

gpt-5-nano

. OpenAI even uses a "smart router" that automatically directs your query to the best model for the job based on complexity.

So, it's ENTIRELY possible that when you're talking to "GPT-5," you might be interacting with a component that shares a significant architectural heritage with the late-stage GPT-4.5 model. The line between where one model ends & the next begins can be blurry, especially in the transition phase.

The model's response that it's a "modified version of GPT-4.5" could, in a weird way, be technically accurate from a certain point of view. It's a new system (GPT-5) built upon the foundation & learnings of its immediate predecessor (GPT-4.5). The model is just articulating its own lineage based on the patterns it has learned.

How This Plays Out in the Real World for Businesses

This whole situation is a fantastic reminder of the importance of grounding AI in specific, accurate data—especially for businesses.

Imagine you're a company using a powerful AI to interact with your customers. If the AI's knowledge is just based on the wide, wild internet, it might give out-of-date information, reflect weird internet biases, or get its own purpose confused.

This is why having control over the AI's knowledge base is so critical. For instance, a platform like Arsturn helps businesses solve this exact problem. It allows you to create custom AI chatbots that are trained specifically on your data. You upload your company's documents, website content, product manuals, & FAQs. The result is an AI assistant that provides instant, accurate answers about your business, not one that makes educated guesses based on the entire internet.

When a customer asks a question, the Arsturn-powered chatbot doesn't need to guess. It pulls from the curated knowledge you provided, ensuring the answers are always correct & on-brand. It's a way to harness the power of this incredible technology without the risk of it going off the rails & confusing your customers. For lead generation, website engagement, & 24/7 customer support, having a chatbot that knows who it is & what its job is makes all the difference.

So, Why the Confusion? It's a Feature, Not a Bug.

To wrap it all up, your GPT-5 model thinks it's a modified GPT-4.5 for a few key reasons:

Outdated Training Data: Its "brain" was formed before the official "GPT-5" name was widely used, so its knowledge base is dominated by information about its predecessor, GPT-4.5.
It's a Predictor, Not a Knower: The model is just generating the most statistically likely response, & "GPT-4.5" was the common term for a long time.
Architectural Lineage: It likely does share a lot of its underlying architecture with the final versions of GPT-4.5, making the statement technically plausible from its perspective.
Internet Chatter Becomes Reality: All the online discussions & speculation about the transition from GPT-4.5 to GPT-5 were absorbed during training, creating a blurry sense of identity.

It's a pretty fascinating look into the quirks & complexities of working with AI at this scale. It’s not sentient, & it’s not confused in a human way. It’s just reflecting the world it was shown.

Hope this was helpful & cleared things up a bit! Let me know what you think.