2017 Bentley Bentayga 6.0L Petrol · ARA 111N

The Bentley Bentayga presents a clean roadworthiness status with a stable maintenance trend across its recorded MOT history. The most recent test on 12 December 2025 at 63,680 miles, roughly seven months before the current date of 16 July 2026, returned a pass with no defects recorded. Prior examinations on 1 November 2024 at 50,986 miles, 8 November 2023 at 44,072 miles, 18 November 2022 at 34,294 miles, and 4 August 2022 at 32,628 miles each achieved passes without advisories. This unbroken sequence of fault-free results indicates consistent upkeep by previous keepers. Mileage accumulation aligns with typical use for a nine-year-old vehicle. From the first recorded test in August 2022 at 32,628 miles, the odometer advanced to 63,680 miles by December 2025, a gain of 31,052 miles over roughly 40 months. That equates to about 9,300 miles per year during this period, marginally above the lifetime average of 7,076 miles per year implied by the 2017 build. No significant gaps appear; the twin tests in 2022 merely reflect a change of ownership or routine scheduling. The steady annual increments suggest the car was not subjected to prolonged standing or excessive commercial duty. The absence of recorded advisories means the tester flagged no worn suspension bushes, corroded structural members, binding calipers, or deficient brake discs at any interval. Such a spotless history implies previous owners addressed consumable items before each examination. A buyer should still commission an independent inspection of the air suspension components, drivetrain mounts, and underbody for corrosion given the vehicle's luxury mass and age. Verify tyre tread depth and brake disc condition in person, as MOT snapshots can miss incremental wear between tests. Previous keepers clearly prioritised statutory compliance; the clean sheet across five tests signals basic upkeep was never skipped. Physical verification of exhaust emissions and fluid leaks remains sensible given the W12 or V8 powertrain complexity.