Benchmark profile
Abstraction and Reasoning Corpus for AGI v3 (ARC-AGI-3)
An interactive successor to ARC-AGI-2 that evaluates whether an AI agent can learn unfamiliar task mechanics through action and feedback.
Data verifiedBenchmark score on ARC-AGI-3 — July 23, 2026
BenchLM mirrors the published score view for ARC-AGI-3. GPT-5.6 Sol leads the public snapshot at 7.8% , followed by GPT-5.6 Terra (0.8%) and GPT-5.6 Luna (0.2%). BenchLM does not use these results to rank models overall.
GPT-5.6 Sol
OpenAI
gpt-5-6-sol
GPT-5.6 Terra
OpenAI
gpt-5-6-terra
GPT-5.6 Luna
OpenAI
gpt-5-6-luna
Benchmark score table (3 models)
ScoreThe published ARC-AGI-3 snapshot places GPT-5.6 Sol first at 7.8%. The third row is 7.6 points behind. The broader top-10 range is 7.6 points, so many of the published results sit in a relatively narrow band.
3 models have been evaluated on ARC-AGI-3. The benchmark falls in the Reasoning category. This category carries a 17% weight in BenchLM.ai's overall scoring system. ARC-AGI-3 is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About ARC-AGI-3
Year
2026
Tasks
Interactive game-like tasks with hidden rules
Format
Agentic task completion under a capped evaluation budget
Difficulty
Frontier agentic reasoning
ARC-AGI-3 is distinct from ARC-AGI-2: it measures interactive, agentic reasoning rather than static grid-puzzle completion. BenchLM tracks published ARC Prize results as display-only until broad, comparable coverage supports a dedicated ranking lane.
BenchLM freshness & provenance
Version
ARC-AGI 3
Refresh cadence
Static
Staleness state
Current
Question availability
Private interactive tasks with public aggregate results
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
FAQ
What does ARC-AGI-3 measure?
An interactive successor to ARC-AGI-2 that evaluates whether an AI agent can learn unfamiliar task mechanics through action and feedback.
Which model scores highest on ARC-AGI-3?
GPT-5.6 Sol by OpenAI currently leads with a score of 7.8% on ARC-AGI-3.
How many models are evaluated on ARC-AGI-3?
3 AI models have been evaluated on ARC-AGI-3 on BenchLM.
Compare Top Models on ARC-AGI-3
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