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
Try spelling things phonetically (example: faux net tick alley), that's one of my benchmarks that AI fails almost every time.
If the input is at all long, or purposefully includes a lot of words about a specific unrelated theme to the coded message, it's impossible.
Wait, I thought phonetically (example: papa hotel oscar novermber echo tango india charle alfa lima lima yankee) meant using a phonetic alphabet, not using word(s) with the same Soundex encoding.
Hooked on phonics.
Yeah, there was some phonics in my primary school education, and I continue to approach new words in that way sometimes. But, they said Phonetically.
Phonetics is the study of speech sounds. The phonetic alphabet is called that because each letter/word in the alphabet was chosen to be one that started with the corresponding phoneme and that the set of words were between them phonetically unambiguous. Phonics is a way of teaching reading and writing that is based on the phonetics of words and how they relate to the written form.
Oh that's an interesting challenge.
I hear some LLMs now have some solutions for the classic "how many Rs in 'strawberry'" problem (related to the tokenization processes), but I have no idea how they might solve the phonetic thing. I'm sure some smart people will eventually find a way though