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
You’re discounting the fact that a human reading Wikipedia will attribute intonation and tone to the text to give further context and meaning. I think the analogy is good. Its not precise but it is the same thing.
I do think AI has a useful purpose and is here to stay. I don’t think it’s groundbreaking like the AI companies want us to think. The bubble will burst and then we’ll see where the cards lie.
OpenAI has lost their lead and I expect they will start to struggle with further funding. There are quite a few warning signs. The price of oil is likely to increase power prices generally and cause construction delays and cost rises. Both will hamper their plans. They still don’t have a viable model for profit.
The analogy is terrible and is not at all, once again, what llms do.
This is an objective fact I have provided evidence to support this.
How are you saying the analogy is good?
Ana analogy does not need to be precise. It expresses a comparison for easier understanding. It is not what LLMs do. However what you’ve expressed is simplified also. So by your standard, it is not useful for the discussion.
So maybe get your head out of your ass and try to understand what people are trying to express instead of correcting them when they are not incorrect.
If precision was of that much importance to you, you would have a different opinion of LLMs.