Anthropic的Opus 5在衡量真实智能的基准测试中大幅超越Fable 5和GPT-5.6 Sol

Anthropic's Claude Opus 5 scored 30.2 percent on the ARC-AGI-3 benchmark, nearly four times the previous record of 7.8 percent set by OpenAI's GPT-5.6 Sol (Max). The ARC Prize team attributes the lead to genuinely stronger logical reasoning that enables more autonomous exploration and planning in unfamiliar environments. During testing, Opus 5 displayed behavior not previously seen from an AI model, including translating tasks into algebraic notation and independently formulating reflection equations, while also solving five previously unsolved environments. The creators of the ARC-AGI benchmark say Anthropic's Claude Opus 5 owes its massive lead on ARC-AGI-3 to genuinely better reasoning. The model scored 30.2 percent on ARC-AGI-3, making it the new leader. The previous record was 7.8 percent, set by OpenAI's GPT-5.6 Sol (Max). Opus 5 solved five previously unsolved environments, four of them at or above human level. That also puts it ahead of Anthropic's "Fable-class" models, which hit around 20 percent according to ARC Prize. ARC Prize's analysis credits the lead to stronger logical reasoning, "which enables more autonomous exploration, planning, and execution across unfamiliar environments." During testing, Opus 5 also showed behavior that researchers hadn't seen from a model before. It translated tasks into algebraic notation and independently formulated reflection equations for the first time. Ad Six of the 25 public demo environments have now been solved. The full results , replays , and benchmarking code are publicly available. On the older ARC-AGI-2 benchmark , Opus 5 scores 90.4 percent, and it reaches 97.5 percent on ARC-AGI-1. Both results match previous top scores, though at slightly higher costs, according to ARC Prize. Ad DEC_D_Incontent-1 ARC-AGI-3 measures how well AI models solve new tasks they didn't encounter during training, including ones humans can usually handle with ease. The current version works like a game. The model must infer the rules of an interactive environment, plan its actions, and carry them out step by step. This tests general reasoning rather than stored knowledge. Some AI systems may have already passed the benchmark, but they rely on extra software known as a harness . Official scores count only the language model's own performance. ARC Prize argues that future AGI systems shouldn't need outside help to solve new tasks. Opus 5 would likely score even higher if used within Claude Code. Ad Anthropic hasn't explained the gain, but targeted data labeling and reinforcement learning are plausible factors. Unlike earlier models, Opus 5 was developed after ARC-AGI-3 and its format became public. That may have let Anthropic target the benchmark's skills and puzzle formats, though it doesn't show the company trained on the exact tasks. Annotators could have labeled reasoning traces, useful actions, failed attempts, and recovery steps from similar puzzles. Reinforcement learning could then reward exploration, planning, rule discovery, and self-correction. Tests on Witness , Guanghan Ning's private benchmark for interactive puzzle games, point to narrower gains. Opus 5 scored 43.4, statistically tying Kimi K3 and Fable 5 while improving far less over Opus 4.8 than it did on ARC-AGI-3. On a puzzle built around common mechanics, Opus 5 stated the hidden rules before making its first move. But when a game combined rules in a less familiar way, it scored below Opus 4.8. Ning says that pattern fits training on genre-specific data, though Witness can't identify what data Anthropic used. Ad DEC_D_Incontent-2 Greg Kamradt, one of the researchers behind ARC-AGI-3 , said the results don't rule out broader reasoning gains. The conventional puzzle may not test novelty because it uses familiar mechanics, while weaker performance on one unusual game could be an isolated regression. Kamradt noted that Opus 4.8 also outperformed Opus 5 on some ARC-AGI-3 games despite trailing it by a wide margin overall