The GPT-5 IQ Score Flap: Why Human Tests Don't Work for AI & What Really Matters
You might have seen the headlines floating around on Reddit & other forums: "GPT-5 Scores a Shockingly Low 70 on an Offline IQ Test!" It’s the kind of news that makes you blink twice. A score of 70? For the successor to the mighty GPT-4? Honestly, it sounds more like a punchline than a benchmark. And another rumor even pegged it at a dismal 57.
So, what's the REAL story here? Is the latest & greatest from OpenAI actually… not that smart?
Here's the thing: those scores are, to put it mildly, not the whole picture. They seem to originate from some pretty informal "offline tests," maybe from a single website, & are hardly the rigorous, peer-reviewed analysis you'd expect for a model of this caliber. The chatter in the comments sections of these posts is actually more revealing than the scores themselves. People are quick to point out that the testers probably used a "non-thinking" version of the model, or that the tests weren't designed to handle the way these massive AI systems actually process information.
And that, right there, is the crux of the issue. The whole idea of giving an AI a human IQ test is a bit like trying to measure a fish's intelligence by asking it to climb a tree. It’s a flawed premise from the get-go.
So, let's break down why these IQ scores are basically meaningless for AI & then dive into how these models are ACTUALLY tested. Because the real story is WAY more interesting & complex.
Why IQ Tests are a Terrible Way to Measure AI Intelligence
First off, let's be clear: IQ tests were designed for humans. They're built around human cognitive frameworks, our cultural norms, & our specific ways of problem-solving. They test for things like working memory, verbal reasoning, & visual-spatial skills in a very human-centric way.
Here’s a few reasons why this just doesn't translate to AI:
Cultural & Cognitive Bias: IQ tests are notoriously biased towards certain cultural and educational backgrounds. An AI, trained on a colossal dataset from the internet, doesn't have a "background" in the same way a person does. It has a fundamentally different, and VASTLY larger, knowledge base. Trying to fit that into a human-centric test is just weird.
The "Cheating" Problem (Sort of): If an IQ test's questions or similar patterns have appeared anywhere in the AI's training data (which is a huge chunk of the public internet), is it really "solving" the problem, or is it just recognizing a pattern it's seen before? Experts point out that AIs can have an unfair advantage here because of their massive memory.
Different Kinds of Smart: An AI can write flawless code in seconds, analyze massive datasets for subtle trends, or even generate creative text formats, but it might "fail" a simple question that requires common-sense reasoning that a five-year-old possesses. AIs make mistakes that are completely alien to us, like thinking 9.11 is greater than 9.9 because of how it tokenizes numbers. This doesn't mean it's "dumb"; it just means its intelligence is structured differently. It's a narrow superintelligence in some areas & surprisingly naive in others.
No Real-World Context: IQ tests are abstract. They don't measure creativity, emotional intelligence, or the ability to collaborate—all hallmarks of human intelligence. An AI's true value isn't in solving abstract puzzles; it's in its application. It’s about how it can help a business streamline its customer service, or a developer debug code, or a marketer draft compelling copy.
This is where the conversation gets practical. For businesses, the "intelligence" of an AI isn't some abstract score. It's about performance & utility. It’s about creating systems that can understand & respond to human needs effectively. For example, a business looking to improve its website engagement doesn't care if its AI can pass a Raven's Progressive Matrices test. It cares if the AI can answer a customer's question about a product instantly & accurately. This is where tools like Arsturn come in. Arsturn helps businesses build no-code AI chatbots trained on their own data. This means the AI isn't just a generic smarty-pants; it's an expert in your business, capable of providing personalized customer experiences & boosting conversions 24/7. That's a kind of intelligence that an IQ test could never measure.
So, How is GPT-5 Actually Measured? The Real Benchmarks
If IQ tests are out, what's in? The AI research community uses a suite of much more sophisticated & relevant benchmarks to evaluate models like GPT-5. These aren't single, catch-all tests but a battery of exams that probe different capabilities. Here are some of the big ones that OpenAI & others have highlighted for GPT-5:
GPQA (Graduate-Level-Plus Question-Answering): This is a TOUGH one. It's made up of PhD-level questions in science, and it's designed to be resistant to "cheating" through search. GPT-5 Pro scores an impressive 89.4% with Python tools, showing a significant leap in advanced reasoning.
SWE-bench (Software Engineering Benchmark): This benchmark tests a model's ability to solve real-world software engineering problems from GitHub. GPT-5 scores a 74.9%, a big jump from previous models & a clear indicator of its improved coding abilities.
MMMU (Massive Multi-discipline Multimodal Understanding): This isn't just about text. The MMMU benchmark tests a model's ability to reason about images, diagrams, & text together at a college level. GPT-5 sets a new state-of-the-art here with a score of 84.2%.
AIME (American Invitational Mathematics Examination): A test of competition-level math skills. GPT-5 scores a whopping 94.6% even without tools, showcasing its raw mathematical prowess.
HLE (Humanity's Last Exam): A super-challenging set of 2,500 questions at the PhD level. The Pro version of GPT-5 scores 42.0%, showing that even the best models have room to grow on these frontier-level problems.
What you see here is a much more granular & meaningful picture. We're not just asking "Is it smart?" We're asking "How good is it at coding? At scientific reasoning? At understanding visual information?" These are the questions that actually matter for real-world applications.
Understanding the GPT-5 Family: It's Not Just One Model
Another reason the "low IQ score" rumor is misleading is that it treats GPT-5 as a single, monolithic entity. But it's not. OpenAI has released a whole family of GPT-5 models, each designed for different purposes:
- GPT-5: The main, base model, designed for complex logic & multi-step tasks.
- GPT-5-mini: A lighter version for when cost is a factor.
- GPT-5-nano: An even smaller model optimized for speed & low-latency applications.
- GPT-5 Pro: The top-tier model for the most challenging tasks, with enhanced reasoning.
On top of this, there's the concept of a "thinking" mode. The new GPT-5 system has a "real-time router" that decides whether to give a quick, surface-level answer or to engage in deeper, slower reasoning depending on the prompt's complexity. This is likely the "thinking" vs. "non-thinking" distinction that the Reddit threads were hinting at. A quick, non-thinking response might be great for a simple query but would naturally perform poorly on a complex reasoning test.
This multi-model approach is all about efficiency & tailoring the AI to the task at hand. It's a smart design that reflects a mature understanding of how AI is used in the real world. For a business, this is HUGE. Imagine you're using an AI for customer support. For a simple question like "What are your business hours?", you want a fast, low-cost response from a model like GPT-5-nano. But for a complex troubleshooting query, you'd want the deep reasoning power of GPT-5 Pro.
This is exactly the kind of nuanced AI strategy that businesses need to be thinking about. And it’s the philosophy behind platforms like Arsturn, which allows businesses to create custom AI chatbots. You can train a bot on your specific knowledge base, so it’s not just pulling from a generic pool of information. It becomes a specialized agent for your business, capable of handling everything from lead generation to providing instant, personalized support. This is the future of business automation—not a one-size-fits-all AI, but a tailored, intelligent system that understands your unique context.
The Takeaway: It's About Utility, Not Vanity Metrics
So, let's circle back to that "shockingly low" IQ score. At the end of the day, it's a distraction. It's a fun but ultimately useless piece of trivia that tells us more about the limitations of IQ tests than it does about the capabilities of AI.
The real story of GPT-5 isn't about some arbitrary number. It's about a significant leap in practical, measurable skills—in coding, in scientific understanding, in multimodal reasoning. It's about a more sophisticated approach to deploying AI, with different models for different needs.
For those of us who are building with AI or using it to grow our businesses, these are the developments that matter. We're moving past the novelty of "look what this AI can do" & into an era of "look what we can do with this AI."
Hope this was helpful & cleared up some of the confusion. Let me know what you think