Benchmark Almanac

AGI Score (A Definition of AGI)

GPT-5 57% Not in model cards

Experts test models on ten cognitive domains from Cattell-Horn-Carroll theory, such as reasoning, memory and perception, with adapted human psychometric tests. Each domain counts 10% toward a score out of 100%.

The paper scores GPT-4 at 27% and GPT-5 at 57%. Long-term memory storage was the weakest domain. The CAIS dashboard shows the score under Automation.

In the authors’ words

The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

Abstract of A Definition of AGI (arXiv:2510.18212, CC0)
Made by Center for AI Safety and collaborators (Hendrycks et al.) Released Oct 2025 Composite indices Last model added Aug 2025 Scores refreshed 2 Nov 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Graded by people Human experts grade or compare the outputs.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
2
Models scored on its own leaderboard

GPT-5 leads with 57%

Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.

  1. 1 GPT-5 57%
  2. 2 GPT-4 Mar 2023 27%
#ModelScoreSettingDateSource
1 GPT-5 57% Aug 2025 Official leaderboard
2 GPT-4 Mar 2023 27% Mar 2023 Official leaderboard

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% 2023 2024 2025 Near the ceiling (85%) GPT-5 GPT-4 0% 20% 40% 60% 80% 100% 2023 2024 2025 Near the ceiling (85%) GPT-5 GPT-4

No lab model card reports it yet

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

More in Other

All 41 in Other
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