Bijan Bowen compares GPT-6 Astra and Claude Fable 5.1 at maximum reasoning effort across five practical tasks, including physical hardware. Both use paid subscriptions, but the work spans several days and usage is not strictly controlled. The results show what happened in these individual trials rather than establishing a universal benchmark ranking.
Bijan Bowen finds stronger presentation in Astra’s toy-car track simulation and more convincing examples of some collision behavior in Fable’s version, although both have modeling or tuning defects. For native Apple Vision Pro game ports, Astra finishes much sooner, while Fable offers more enjoyable hand-based movement and weapon control. Each model ports a different game it previously created, which limits direct comparability.
Bijan Bowen gives Astra the robot-arm test: using images from a fixed, awkward camera angle, it moves an orange toy car in about 40 minutes through repeated observation and small adjustments. Fable takes a more elaborate calibration and spatial-mapping approach but does not complete the task before the attempt is stopped after roughly 85 minutes to conserve usage. This establishes a result under that stopping condition, not an inability to solve the task with more time.
Bijan Bowen also asks both models to adapt the same game to a small ESP32 touchscreen device. Astra preserves more visual detail and draw distance but shows lower frame rates and jitter. Fable simplifies geometry and limits distant rendering to achieve smoother play. Both add touch controls, and the balance between fidelity and responsiveness leaves this test without a clear winner.
Bijan Bowen prefers Fable’s Blender-and-Godot cinematic repair game for its richer environments, interactions and narrative, despite an initial menu bug and some actions requiring the requested skip feature. Astra’s game is playable but uses a less striking isometric presentation. The final assessment is effectively a tie, with different strengths and practical costs depending on the task.
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