GoKawiilOpenAI's GPT-6 Astra on ARC-AGI-3
Summary
GPT-6 Astra scores 62.7% for $26K on ARC-AGI-3 Semi-Private with our Standard harness Standard harness enables a model to carry forward notes it chooses to keep with it throughout the environment. , and 99.9% for $19K with a Provider Adapter harness The Provider Adapter harness preserves opaque reasoning state between requests and uses compaction for longer conversations, allowing the model to reuse prior work. .
, and 99.9% for $19K with a . GPT-6 Astra surpasses the human baseline in action efficiency on ARC-AGI-3. It used fewer actions than the median tested human on 96% of levels.
A key behavior observed in GPT-6 Astra was its ability to turn unfamiliar environments into compact symbolic world models. It represented game mechanics as logical rules and developed its own domain-specific language shorthand to track state and plan actions.
ARC-AGI-3
ARC-AGI-3 is a benchmark for studying agentic intelligence through novel, abstract, turn-based environments. Agents must explore, infer goals, and build internal models of environments to effectively plan actions without explicit instructions. You can play ARC-AGI-3 yourself.
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These environments only contain core knowledge priors and are difficulty-calibrated through controlled testing with human participants. Humans can solve 100% of the environments.
The goal of the ARC-AGI series is to measure the “residual gap” between current artificial intelligence and AGI. We define AGI as a system’s ability to acquire any skill a human can, as efficiently as a human can.
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