OpenAI's GPT-6 Astra on ARC-AGI-3
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
OpenAI 相关模型动态已经出现，适合跟踪能力变化、生态影响和后续可用性。
- 判定依据
- 热度约为该来源近期上榜条目中位水平的 2.0 倍
- 指标对比
- 108 vs 中位 54（20 条基线样本）
- 检出时间
- 09/03 22:01
OpenAI 的 GPT-6 Astra 在 ARC-AGI-3 上进行了评估，展示了其在上下文管理功能方面的表现。Provider Adapter harness 允许 Astra 在请求之间保留不透明的推理状态，并使用压缩来管理更长的对话，从而使模型能够重用之前的工作。这种方法有助于有效管理对话和维护上下文，性能指标显示在不同推理努力水平下有不同的百分比，例如最大努力为 62.7%，低努力为 17.5%。
Published
03 Sep 2026
Summary
- GPT-6 Astra scores 62.7% for $26K on ARC-AGI-3 Semi-Private with our 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 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 .
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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.
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
GPT-6 Astra achieves state-of-the-art scores on ARC-AGI-3 with both the Standard and Provider Adapter harnesses. Higher reasoning levels generally cost less because Astra solves games in fewer actions, reducing the total number of model calls and tokens. View the full results . With our 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 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 enables a model to carry forward notes it chooses to keep with it throughout the environment. 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