In March 2023, OpenAI announced that GPT-4 had scored in the 90th percentile on the Uniform Bar Exam, a figure that quickly became shorthand for how close large language models were getting to professional-grade reasoning. A 2024 peer-reviewed re-analysis of the same results found the real ranking closer to the 48th percentile against the general pool of test-takers, once the comparison group and scoring method were corrected. Nothing about the model changed between those two claims — only how the score was interpreted. According to AI Business, that gap is exactly what separates artificial intelligence from artificial intellect: one is a measurable output, the other is a capacity for judgment.
The terms get used interchangeably in product marketing, but they describe different things. "Intelligence," in the way the industry measures it, is the capacity to produce a correct output for a given input — pass a test, generate working code, translate a sentence. "Intellect" is something closer to judgment: knowing when a rule doesn't apply, recognizing the edges of your own competence, and adapting when the situation doesn't match anything you've seen before. Most of what gets sold as AI progress today is progress in the first category. Very little of it is progress in the second.
That distinction is not academic for anyone building on top of these models. It determines what you can safely automate and what still needs a human holding the wheel.
What benchmark scores are actually measuring
Standardized tests, coding challenges, and multiple-choice exams share one property that makes them ideal training targets: they have a fixed, checkable answer. That makes them gradient-friendly — a model can be tuned directly against them, and improvement is easy to measure and market. It's also why leaderboards for benchmarks like MMLU, GPQA, and SWE-bench have climbed so fast in the last two years.
The problem is that a fixed-answer test rewards pattern completion, not judgment. A model can score well by having seen enough similar problems during training, or by exploiting regularities in how exam questions are phrased, without possessing any general capacity to reason about a genuinely new situation. Benchmark contamination — where test questions or close variants leak into training data — has been a recurring concern precisely because it inflates scores without improving anything a user would notice in production.
Where intellect — judgment under novelty — breaks down
The gap shows up whenever a task steps outside the shape of its training distribution. Language models still routinely produce confident, fabricated citations, invent plausible-sounding facts, or fail elementary logic puzzles when they're phrased in an unfamiliar way, even after acing formal exams that test the same underlying skill. Multi-step agents are a sharper example: they can execute long tool-use chains correctly, but often keep pursuing a failed approach for many steps instead of noticing the failure and revising course — the kind of self-monitoring that intellect implies and pure pattern-matching does not.
None of this means the systems are useless — it means they're good at a narrower thing than "intelligence" as a word implies.
What this means for anyone building on these models
For teams shipping AI products, the practical upshot is about where to place trust:
- Treat benchmark rank as a measure of task-specific competence, not a proxy for reliability on your specific, unbenchmarked workflow.
- Evaluate on cases that fall outside the training distribution your vendor is likely to have used, not just held-out sets that resemble it.
- Keep a human or a hard-coded check in the loop for decisions that require judgment about novel or ambiguous situations — contract interpretation, medical triage, financial risk calls — rather than fact retrieval or formatting.
- For agentic systems, build in explicit failure-detection steps, since models rarely notice on their own that a multi-step plan has stopped working.
This is also a useful filter for evaluating vendor claims. A model that tops a leaderboard is telling you it's good at that leaderboard's tasks. It's not telling you it will notice when your production data looks nothing like the benchmark.
AiiN's takeaway
The "intelligence vs. intellect" framing is a useful corrective to how the industry talks about progress, because it separates two things that get bundled into one marketing number. Scaling has produced systems that are extraordinarily good at intelligence-type tasks — retrieval, pattern completion, formatted output — and it has produced much more modest, harder-to-measure gains in intellect-type tasks — judgment, self-correction, reasoning about genuinely unfamiliar situations. In our estimation, that second category is likely to remain the bottleneck for how much autonomy builders can hand these systems, regardless of how high the next benchmark score climbs.