The ARC Prize organization designs benchmarks which are specifically crafted to demonstrate tasks that humans complete easily, but are difficult for AIs like LLMs, “Reasoning” models, and Agentic frameworks.
ARC-AGI-3 is the first fully interactive benchmark in the ARC-AGI series. ARC-AGI-3 represents hundreds of original turn-based environments, each handcrafted by a team of human game designers. There are no instructions, no rules, and no stated goals. To succeed, an AI agent must explore each environment on its own, figure out how it works, discover what winning looks like, and carry what it learns forward across increasingly difficult levels.
Previous ARC-AGI benchmarks predicted and tracked major AI breakthroughs, from reasoning models to coding agents. ARC-AGI-3 points to what’s next: the gap between AI that can follow instructions and AI that can genuinely explore, learn, and adapt in unfamiliar situations.
You can try the tasks yourself here: https://arcprize.org/arc-agi/3
Here is the current leaderboard for ARC-AGI 3, using state of the art models
- OpenAI GPT-5.4 High - 0.3% success rate at $5.2K
- Google Gemini 3.1 Pro - 0.2% success rate at $2.2K
- Anthropic Opus 4.6 Max - 0.2% success rate at $8.9K
- xAI Grok 4.20 Reasoning - 0.0% success rate $3.8K.
(Logarithmic cost on the horizontal axis. Note that the vertical scale goes from 0% to 3% in this graph. If human scores were included, they would be at 100%, at the cost of approximately $250.)
https://arcprize.org/leaderboard
Technical report: https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf
In order for an environment to be included in ARC-AGI-3, it needs to pass the minimum “easy for humans” threshold. Each environment was attempted by 10 people. Only environments that could be fully solved by at least two human participants (independently) were considered for inclusion in the public, semi-private and fully-private sets. Many environments were solved by six or more people. As a reminder, an environment is considered solved only if the test taker was able to complete all levels, upon seeing the environment for the very first time. As such, all ARC-AGI-3 environments are verified to be 100% solvable by humans with no prior task-specific training
Definitely! I am one :) but I still desire the presence of friends from time to time (and usually in small groups).
Yup! There’s always a nonzero chance you’re not as healthy as you think you are (let’s call it the quantum theory of health: everyone is in a superposition of being both healthy and unhealthy at the same time), especially as we change due to age, making us unfamiliar with our own bodies… I’d tell you about my own challenges here, but that’d be TMI.
And, yes, that’s why we go to regular checkups with someone who has a better perspective to judge “healthiness” (side note: doctors aren’t perfect, so visiting them too frequently can be worse than never at all; there’s a “healthy” cadence to checkups).
This boils down to the definition of “healthy”. It even becomes a philosophical question that’s really hard to answer… Is it healthy to live a sedentary lifestyle? Is it healthy to exercise too much? Is it healthy to not know TIPP, in case you (or a loved one) gets a panic attack? Is it healthy to ignore yourself? Ignore others? Is it healthy to mention quantum superposition in a conversation about health? ;)
But, yes, I agree. Life’s as messy and diverse and as hard to sum up as everybody whose ever lived, but yet we carry on … I hope that’s healthy.
Edit: typo, and missing a hint that I’m making a joke about me over-generalizing physics concepts
My entire point is that you are just overgeneralizing, in general, and saying rather silly things.
Fair enough; the Internet is a silly place full of distracted, armchair philosophers. However, my entire point was that an LLM doesn’t rely on machinery in the same way that a human brain does. That doesn’t make AI “worse” or “better” overall, but it does make it an awful replacement for humans.