OpenAI GPT-6 Astra, ARC-AGI-3 벤치마크 최고 성능 달성

By Greg Kamradt Published 03 Sep 2026 OpenAI'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. . 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 . 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. ARC-AGI-3 is the third generation of the ARC-AGI benchmark series . It tests agentic capabilities beyond ARC-AGI-1 and ARC-AGI-2 . Each generation expands on the one before it - as frontier AI capabilities advance, our benchmarks must advance with them. ARC-AGI-3 tests four components of agentic intelligence: Exploration: In real-world environments, information is rarely provided passively. Agents must actively obtain it by interacting with their surroundings. Modeling: Agents must turn raw observations into a generalizable model that can predict future states and outcomes. Goal-setting: Agents must identify target future states with only sparse rewards. Planning and execution: Agents must map a path from their current state to a goal, course correcting as new information appears. Astra Results With our Standard harness Standard harness enables a model to carry forward notes it chooses to keep with it throughout the environment. , OpenAI’s Astra (max) scores 62.7% on ARC-AGI-3 Semi-Private for $26K. With the 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. , Astra (high) scores 99.9% for $19K. Both are state-of-the-art scores. See the full leaderboard . At max reasoning effort, Astra solves games more efficiently, requiring fewer actions and therefore lowering total cost relative to the other reasoning-effort levels. Reasoning effort Standard harness Standard harness enables a model to carry forward notes it chooses to keep with it throughout the environment. 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. max 62.7%, $26,098 98.6%, $17,332 xhigh 59.3%, $37,317 98.4%, $18,147 high 54.8%, $40,705 99.9%, $18,817 medium 38.6%, $48,090 98.4%, $19,285 low 17.5%, $38,166 98.0%, $21,298 none 35.2%, $49,791 96.7%, $23,457 For a cost comparison, during our controlled testing, human participants were paid $115 per 90-minute session, plus $5 per game completed. Participants attempted approximately nine games per session, roughly $12.78 per attempted game before bonuses. Most of this fee pays for the participant’s time and willingness to take the test, rather than the energy their brain uses (a closer proxy to compare with AI). If we look at only the brain’s energy, and price it as electricity, the estimate drops to about 0.6 cents per session, or 0.067 cents per game attempted. 1 Analysis Beyond the scores, Astra’s replays show how it turns unfamiliar game mechanics into useful working models. Three findings stood out: the compact algebraic notation it develops, its action efficiency compared with humans, and the custom tools it builds. Custom Algebraic Notation When playing ARC-AGI-3, Astra chooses which strategy notes it would like to carry forward. It tracked objects, coordinates, rules, and unfinished plans, while also using a custom domain-specific language notation it generated for the environments. We’ve seen similar behavior in other models , but Astra’s notes stood out for their precision and information density. It distilled the scene into a compact code-like symbolic model: where objects were, how they interacted, and exactly which actions needed to happen in what order. This is an on-the-fly algebraic shorthand rather than a fully fledged programming language. For example: Game state: L8: hub q2 (8↓). Lengths: 14=1… records the level, a local rotation index, and mechanism lengths. s5i5, frame 219 Multi-step plans: extend8 to3; retract10 to2; shorten8 to1 records an ordered sequence of changes to the color-8 and color-10 mechanisms. s5i5, frame 219 Controls and coordinates: 9−=(39,4), rotate=(49,18), 14+=(59,11) maps operations to the coordinates of the controls that perform them. s5i5, frame 235 Time and position: Turn 5: P=(24,20), empty, facing west combines a turn counter with the player’s location, carrying state, and orientation. wa30, frame 708 Action Efficiency Compared to Humans Before launching ARC-AGI-3, we tested approximately 500 members of the general public to establish a human baseline for action efficiency, or simply, how quickly did people solve each environment. Participants were not selected for puzzle-solving experience or ability. 2 For each level, we defined the “human baseline” using the median action count among players who completed it. This gives us a reference for comparing human and AI performance. An AI that needs more actions is less action-efficient, while one that needs fewer actions is more action-efficient. In the 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. , Astra (max) used fewer actions than the human baseline on 96.0% of levels and used 51.7% fewer actions per level on average . This is a material milestone. This means by ARC-AGI-3’s measure of action efficiency, Astra matched and surpassed human parity. As an aside, before we launched ARC-AGI-3, we hypothesized that action efficiency would remain a dividing line between humans and AI. We anticipated that even when an AI solved an environment, it might require substantially more exploration (actions) than a person. That remains true of brute-force approaches, but frontier AI shows a more binary-like pattern. Once frontier AI “understands” the mechanics, it generally executes within the range of human efficiency. The plot above compares the number of actions Astra used to complete each level with our human baseline. This reinforces why ARC-AGI-3 measures action efficiency, not just task completion. A completion-only score would tell us that Astra completed an environment, but not how efficiently it learned to solve them. Most benchmarks only measure cost efficiency, which measures the computational resources used, but action efficiency measures how much experience with an environment was required. Astra’s results show that it needed fewer interactions than the human baseline to execute a solution. Custom Tools in Agent Harness We also evaluated Astra in the PRO-LONG harness ( paper ), an early ARC-AGI-3 red-teaming partner. In this advanced setup, Astra had access to a sandbox where