Introduction
Is ChatGPT anywhere close to ChatGPT anywhere close to general intelligence, or are we still dazzled by fluent text? The new conversation is not hype. It is measurement. A landmark research effort proposes a rigorous AGI benchmark, then scores modern systems against it. That changes the frame for AGI vs generative AI.
Here is the big idea in plain language. The paper defines AGI as the cognitive versatility and proficiency of a well-educated adult, then tests AI on the same kinds of abilities used in human psychometrics. Not vibe checks, not cherry-picked demos, real cognitive batteries. The result is a single “AGI Score” from 0 to 100. GPT-4 lands at 27 percent. GPT-5 reaches 57 percent. Progress is real. The gap is visible.
This article translates that framework for working engineers, researchers, and curious readers. We will cover what the test measures, how it distinguishes AGI vs generative AI, where today’s models shine, where they fail, and what needs to be built next.
Table of Contents
1. The Confusion We Keep Having, Finally Resolved
The internet loves to argue about AGI vs generative AI. People point to striking coding feats or elegant essays as proof that we are already there. Others point to brittle reasoning and missing memory and say we are nowhere close. Both sides talk past each other because “intelligence” was a moving target.
The new framework pins that target down. It uses long-validated psychometric structure, the Cattell-Horn-Carroll model, to break intelligence into concrete abilities. Then it asks, with no drama, whether a model actually has those abilities. That puts AGI vs generative AI on measurable ground.
2. What The New Framework Actually Measures
In human testing, we do not measure “smartness” as a single blur. We test specific abilities that together produce general intelligence. The paper adopts that playbook for AI. It defines ten core AI cognitive abilities, each worth 10 percent of the total AGI score, to emphasize breadth over specialization. The goal is human-level versatility, not a bag of tricks.
2.1 The Ten Core Abilities At A Glance
These abilities are the backbone of the AGI benchmark. They are also a clean way to separate AGI vs generative AI.
| Ability | What It Means, In Practice |
|---|---|
| General Knowledge (K) | Breadth of factual understanding, including commonsense, culture, science, social science, and history. |
| Reading & Writing (RW) | Consuming and producing written language, from decoding to complex comprehension and composition. |
| Mathematical Ability (M) | Arithmetic through calculus, plus probability and algebraic fluency. |
| On-the-Spot Reasoning (R) | Solving novel problems by flexibly controlling attention and forming new inferences. |
| Working Memory (WM) | Holding and manipulating information across text, audio, and vision during a task. |
| Long-Term Memory Storage (MS) | Continually learning and retaining new information, associative and verbatim. |
| Long-Term Memory Retrieval (MR) | Accessing stored knowledge precisely while resisting hallucination. |
| Visual Processing (V) | Perceiving, generating, and reasoning about images and video. |
| Auditory Processing (A) | Discriminating and working with sound, including speech and rhythm. |
| Speed (S) | Performing simple cognitive tasks quickly and fluently. |
This list comes straight from the paper’s CHC-grounded taxonomy of abilities and their plain-language definitions.
3. Results You Can Read In One Minute
Here is the headline you can repeat without caveats. GPT-4 earns 27 percent on the AGI benchmark. GPT-5 jumps to 57 percent. The scores are the weighted sum across the same ten abilities listed above, each worth 10 percent. This makes comparisons straightforward and makes AGI vs generative AI a question we can answer with numbers.
3.1 The Numbers, Side By Side
| Model | K | RW | M | R | WM | MS | MR | V | A | S | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|
| GPT-4 (2023) | 8% | 6% | 4% | 0% | 2% | 0% | 4% | 0% | 0% | 3% | 27% |
| GPT-5 (2025) | 9% | 10% | 10% | 7% | 4% | 0% | 4% | 4% | 6% | 3% | 57% |
These are the paper’s published scores, not estimates. They capture a strongly “jagged” profile. Strengths, then cliffs.
4. The Jagged Profile, Explained For Builders
If you ship software, you know a performance graph that looks like a city skyline. Peaks where the system is native. Valleys where the scaffolding shows. Today’s large models look like that.
Reading and writing are high. Math is high. Knowledge is broad. Speed is acceptable. But reasoning sits below those peaks, and long-term memory storage is not just low, it is zero. That is the loudest message of the entire study, and it is why AGI vs generative AI is not a semantic debate. It is a capability gap.
5. Why Long-Term Memory Is Still At Zero
Both GPT-4 and GPT-5 show no measurable Long-Term Memory Storage. Not reduced. Zero. Across associative, meaningful, and verbatim memory, the score stays flat. That means no durable acquisition of new knowledge in the way humans learn during use. The models can juggle a long prompt, then forget it. This is the most important constraint on AGI vs generative AI today.
Retrieval looks better. Models can pull a lot from parameters, but they hallucinate, which drags that ability back down in practice. Working memory provides a wide stage for context, yet it acts more like a temporary scratchpad than a long-term store. You feel it anytime a model loses the thread after a long interaction. The paper’s breakdown of storage and retrieval makes that split concrete.
5.1 Working Memory As A Crutch
When long-term storage is absent, working memory and context windows carry the load. That is a costly substitute. It raises token budgets, raises latency, and still fails on tasks that need stable retention. The authors call attention to these structural weaknesses so that progress on AGI vs generative AI is not overstated by surface fluency.
6. What “Generative” Gets You, And What “General” Requires
If you want a clean mental model for AGI vs generative AI, think of talent versus versatility. Generative systems are great at tasks that look like reading, writing, and math over compressed world knowledge. That maps to RW, M, and parts of K. It is why GPT-5 feels brilliant in code, translation, summarization, and analysis.
General intelligence requires a broader kit. On-the-spot reasoning that holds up off distribution. Long-term storage that accumulates personal and procedural knowledge. Cross-modal perception that ties text, images, and audio into a coherent world model. Speed when it helps, restraint when it does not. That is the bridge we must build to turn AGI vs generative AI from a debate into a solved engineering problem.
7. So What Is AGI In AI, Formally?
The paper answers the question most people ask in messy ways. What is AGI in AI? It is an AI that matches or exceeds the cognitive versatility and proficiency of a well-educated adult. Not a superhuman economist. Not a perfect mathematician. A human-level AI across the full spread of abilities. That definition directs both evaluation and research. It also puts the spotlight on memory, reasoning, and perception as the real blockers in AGI vs generative AI progress.
7.1 The Path To AGI, As A Builder’s Roadmap
A credible path to AGI starts with the missing machinery.
- Stable Long-Term Memory
Efficient, safe, and personal memory systems that learn during use. Storage and retrieval must be reliable and resilient to drift. The paper’s zero on storage should ignite focused work, from architectural changes to memory-augmented modules. That is the non-negotiable next step in AGI vs generative AI. - Robust On-The-Spot Reasoning
Move beyond chained prompts that overfit patterns. We need mechanisms that form new abstractions under pressure. That means better attention control, search, and self-critique that are measured against the R domain, not demo tasks. - Multimodal Perception With Spatial Understanding
Vision is not just captioning. It is analysis and spatial reasoning. Audio is not just transcription. It is recognition, rhythm, and memory. Cross-modal tests in the framework point to what is missing and how to check progress honestly. - Speed Where It Matters
Latency is a user feature. But speed without accuracy does not count as intelligence. The S domain makes this a measurable tradeoff instead of a hand-wave.
This is how you turn an AGI benchmark into an engineering plan.
8. GPT-5 Capabilities, Without the Mythmaking
The scores say useful things about GPT-5 capabilities. The model is near the cap in reading and writing. It is strong in math. It shows improved reasoning compared to GPT-4. It adds solid gains in visual and auditory processing, though those categories still have room to grow. Long-term storage remains missing. Retrieval works, but hallucinations erode trust. This is why AGI vs generative AI still matters. The model is a powerhouse in knowledge-based workflows, yet it is not a system that learns and stabilizes like a person.
If you lead a research team, read the numbers as marching orders. Invest in memory. Invest in reasoning that survives novelty. Use the framework’s tasks, not social media clips, to judge results. That is how AGI vs generative AI stops being an internet argument and becomes a lab checklist.
9. Two Useful Tables For Quick Reference
Here are condensed references you can use in planning and reviews.
9.1 Ten Core Abilities And Example Implications
Ability Practical Product Implication
| Ability | Practical Product Implication |
|---|---|
| K | Broad knowledge reduces tool calls and prompt scaffolding. |
| RW | Better docs, safer instructions, cleaner code generation. |
| M | Reliable calculation and formal manipulation under long contexts. |
| R | Real novelty handling, fewer brittle prompt recipes. |
| WM | Longer coherent tasks, better multi-step control. |
| MS | True personalization and continual learning during use. |
| MR | Faster, more precise retrieval, fewer hallucinations. |
| V | Vision tasks beyond captioning, including spatial reasoning. |
| A | Audio understanding and creative tasks with rhythm and speech. |
| S | Latency that matches human workflows. |
These map directly to the paper’s definitions of abilities and their diagnostic role in the evaluation.
9.2 AGI Score Summary, From The Paper
| Model | K | RW | M | R | WM | MS | MR | V | A | S | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|
| GPT-4 (2023) | 8% | 6% | 4% | 0% | 2% | 0% | 4% | 0% | 0% | 3% | 27% |
| GPT-5 (2025) | 9% | 10% | 10% | 7% | 4% | 0% | 4% | 4% | 6% | 3% | 57% |
Numbers and weighting are taken verbatim from the published table. This is the clearest snapshot available of AGI vs generative AI progress today.
10. Where This Leaves Builders, Researchers, And Readers
Let’s answer the core question cleanly. AGI vs generative AI is no longer a taste test. It is a measurement with a definition, a structure, and a score. The definition is human-level versatility across ten abilities. The structure is CHC-grounded testing. The score is a weighted sum that exposes a jagged profile. GPT-5 at 57 percent is impressive. It is also incomplete.
For practitioners, the takeaway is simple. Use today’s models for what they are great at. Reading, writing, math, coding, knowledge applications. Design your systems so they do not pretend to have long-term memory when they do not. If you need persistence, build it. If you need reliability under novelty, test it under the R domain. If you want a human-level AI, attack storage head on.
For researchers, the path to AGI now looks like work, not prophecy. Memory architectures that learn during use. Reasoning mechanisms that truly generalize. Perception that grounds symbols in the world. Evaluation that matches the abilities list. That is how AGI vs generative AI becomes yesterday’s phrase.
For readers and decision makers, insist on numbers tied to abilities, not slogans. Ask how a new feature moves the score on storage, retrieval, reasoning, vision, audio, or speed. Ask what tradeoffs were measured. Ask where the system still fails. The AGI benchmark gives you the language to do that.
Call to action. Treat this framework as your new spec. If you build, align your roadmap with the missing abilities. If you buy, ask vendors to report scores tied to the ten domains. If you teach or write, stop asking whether AGI vs generative AI is hype. Start asking which abilities a system truly has, and what it still needs to become human-level AI. Then go build the parts that move the needle.
Key sources for abilities, definitions, and scores are drawn from the paper’s CHC-grounded framework, its list of ten abilities, and the published results for GPT-4 and GPT-5.
Note on usage. If you quote the scores or ability names in your own writing, cite the paper directly. It will keep debates about AGI vs generative AI grounded in the same shared facts, which is the point of having an AGI benchmark at all.
1) What is the real difference between AGI and the generative AI I use every day?
AGI vs generative AI comes down to scope. Generative AI excels at knowledge-based tasks, like writing and coding, because it patterns over data. AGI targets human-level range, including reasoning, perception, working memory, and long-term memory. One is specialized output, the other is broad cognitive ability.
2) What is the “AGI Score” and how is it measured?
Cattell-Horn-Carroll model, including knowledge, reading and writing, math, reasoning, working memory, long-term memory, visual, auditory, and speed. Each domain contributes to a single percentage that represents progress toward human-level AI. This turns AGI vs generative AI into a measurable comparison.
3) Is GPT-5 considered AGI according to this new benchmark?
No. On this framework, GPT-5 reaches an AGI Score of about 57 percent. GPT-4 sits near 27 percent. The gains are real, yet the model still lacks durable long-term memory and fully robust reasoning, which keeps it below human-level AI across the full cognitive spread. On this measure of AGI vs generative AI, GPT-5 is not yet AGI.
4) What are the biggest weaknesses of current AI models like GPT-5 on the path to AGI?
The profile is jagged. Reading, writing, and math score high. Long-term memory storage is effectively zero, so models rely on large context windows as a temporary crutch. This inflates token use, hurts reliability, and limits true learning. Reasoning under novelty remains uneven. These gaps define the practical boundary in AGI vs generative AI today.
5) Who created this AGI definition and why is it significant?
A group of leading researchers proposed a standardized, quantifiable definition aligned to human psychometrics. That matters because it turns AGI vs generative AI from a debate into measurement, guiding research toward specific bottlenecks like memory, reasoning, and multimodal perception.