OpenAI’s GPT-6 Astra drives Toyota Corolla through parking lot course
A new benchmark called DrivingBench tested whether frontier language models could control a real Toyota Corolla in a parking lot. Only OpenAI’s GPT-6 Astra succeeded, completing a roughly 130-meter course on its second attempt in 5 minutes 22 seconds. The other models—Claude Fable 5.1, Grok 4.6, and GPT-5.6 Sol—failed to progress past the halfway mark in any attempt.
Key points
- OpenAI’s **GPT-6 Astra** completed a **130-meter parking lot course** in **5:22**, the only model to finish
- **Claude Fable 5.1** reached **45%** progress on its third attempt, others failed before halfway
- Models used **comma four** + **openpilot** with **MCP tools** for steering, speed, and braking commands
The test, created by Aditya Ramabadran, Simon Mahns, and Tobias Gessler, required models to navigate cones using a comma four hardware setup with openpilot. Models had to interpret camera feeds, adjust steering and speed, and account for latency. Astra averaged under 1 mph, while Fable 5.1 reached 45% progress on its third try. Failures often stemmed from misreading lane markers or poor command timing. The researchers noted Astra showed in-context learning, adjusting its approach between attempts, while Grok and Sol struggled with perception and control cadence.
The benchmark is open-source, with all runs published as video traces. The team plans to expand it with more models, harder courses, and varied reasoning efforts. They emphasize the need for safer evaluation methods for real-world AI control.
Model pages: GPT-6 Astra → · Claude Fable 5.1 → · Grok 4.6 →
Driving Bench: GPT-6 Astra Becomes First Model To Drive A Real Car Through A Course
officechai.com · 23 September 2026
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