GoKawiilOpenAI's GPT-6 Astra outperforms Claude Fable on robot arm block task
Testers gave OpenAI's GPT-6 Astra control of the same YAM robotic arms used to evaluate Claude Fable 5 and 5.1, under identical Inspect Robots policies and two manipulation tasks. Astra placed a block into a bowl in 19 of 20 trials, far surpassing Fable 5.1's 8 of 20 and Fable 5's 1 of 20, while running roughly 2.7 times faster and cheaper per attempt. On a harder puzzle-piece insertion task, however, Astra matched Fable 5.1 at just 2 successes out of 20, stalling at the same final step.
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The results suggest current AI models can now handle simple pick-and-place robotics tasks reliably and cheaply, but still struggle with fine motor precision needed for tight-tolerance insertions. This gap highlights where robotic foundation models remain unready for real-world manufacturing or assembly work despite rapid gains in basic manipulation.
- GPT-6 Astra achieved a 95% success rate on the block-into-bowl task versus 40% for Fable 5.1 and 5% for Fable 5.
- Astra completed the bowl task at roughly half the cost and well under half the time of Fable 5.1.
- Both Astra and Fable 5.1 succeeded only 2 of 20 times on the precision puzzle-piece task, showing persistent limits in fine manipulation.
Melissa & Doug Wooden Shape Sorting Puzzle Board — The peg-and-groove insertion task that stumps these models is exactly what a classic knobbed shape-sorting board tests: align the piece, orient it, and drop it home. It's a fun, tactile benchmark to try yourself (or with a kid) while you watch robot arms stall on the final millimeter. Sturdy wooden pieces with grab knobs make it easy to handle.
See Melissa & Doug Wooden Shape Sorting Puzzle Board on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: openai.robocurve.org, 2026-09-06
Published there as: “GPT-6 Astra on robot arms”
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