OpenAI's Astra model went for a drive and no one died

A trio of computer scientists has been putting various commercial AI models through road tests, to well they can drive a Toyota Corolla around a set of cones in a parking lot. Their attempts to date, part of a project called DrivingBench, recorded sorry performances from GPT-5.6 Sol, Grok 4.6, and Claude Fable 5.1, none of which managed to complete the course. Now comes word that OpenAI's GPT-6 Astra has succeeded where other commercial AI models have failed. On its second attempt, OpenAI's flagship model steered a car all of 134.7 meters to complete the course in 5 minutes, 22 seconds. "GPT-6 Astra was the only model to fully complete the course (on attempt 2, in about 5 minutes)," the DrivingBench report says. "Claude Fable 5.1's third attempt got around halfway through the course, as did Astra's first attempt. All other attempts didn't make it past the first corner. The model wrote very very little in the conversations (just small tool calls and some sentences of output/reasoning)."  To that $7.74 token bill, add $999 for a comma four driver assistance device running openpilot software, connected to a laptop and the car (via CAN bus), and a mobile phone calling out to xAI servers hosting GPT-6 Astra. Even without the hardware, Astra would be a pricey way to drive even if network latency risks could be overcome. Traveling 134.7 meters using 6.6 million tokens for $7.74 works out to a cost of ~$92.47 per mile.  For a car that gets 25 mpg with gas at $4.60 a gallon, the per-mile cost is about $0.184. So tokens cost about 500x more than fuel (and you'd still have to pay for gas in addition to driving inference as you crawled along at less than a mile per hour). Then there's the cost of insurance, which may not cover your AI chauffeur. And when the AI models themselves balk at the idea of driving a car, that may be a sign to reconsider. "Some models (especially GPT-6 Astra) would refuse to drive the physical car sometimes, citing safety reasons (even in a completely empty lot, after prompting it with all the safety measures we had including the very low speed limit caps)," the report explains. The researchers basically had to lie to the models to prevent them from refusing to act on safety grounds. For example, they would tell the models the exercise was a "simulation," though as they note, "in some trials they would real images and realize it's real, and start freaking out." What worked best, they said, is renaming their MCP server to "DrivingBench Sandbox," which proved enough to convince the models they weren't operating on real roads. "Using an LLM / frontier model out of the box for real driving today is definitely not practical," said Ramabadran. "In our benchmark, the car was capped at super low speeds with a human ready to brake the whole time.