2013 KTM X-Bow R vs 2020 Toyota GR Supra 3.0
AI Telemetry Verdict:In this head-to-head, the 2020 Toyota GR Supra 3.0holds the statistical edge in Performance Index (765). For the technical touge passes of Mount Fuji, the 2013 KTM X-Bow Ris the superior technical chassis due to its refined lateral G-force profile.
2013 KTM X-Bow R
KTM
2020 Toyota GR Supra 3.0
Toyota"In a head-to-head battle, the 2020 Toyota GR Supra 3.0 edges out the 2013 KTM X-Bow R primarily due to its exceptional Performance Index performance."
| 2013 KTM X-Bow R | Metric | 2020 Toyota GR Supra 3.0 |
|---|---|---|
| 765 | Performance Index | 765 |
| 6.1 | Speed | 7.2 |
| 8.2 | Handling | 7 |
| 8 | Acceleration | 6.9 |
| 7.8 | Launch | 7.2 |
| 8.4 | Braking | 7.1 |
| 2.2 | Offroad | 3.5 |
| 155 | Top Speed (MPH) | 165 |
| 1742 | Weight (lbs) | 3400 |
| RWD | Drivetrain | RWD |
| 90,000 | Price (CR) | 55,000 |
📈 Technical Data Analysis:
Speed & Acceleration Analysis
When it comes to straight-line performance, the 2013 KTM X-Bow R boasts a speed rating of 6.1, while the 2020 Toyota GR Supra 3.0 hits 7.2.
The 2020 Toyota GR Supra 3.0 pulls ahead in long stretches, making it a formidable opponent on the Tokyo highways.
Handling & Cornering Dynamics
In the tight technical sections of the Mount Fuji passes, handling is everything. The 2013 KTM X-Bow R features a handling score of 8.2, whereas the 2020 Toyota GR Supra 3.0 manages 7.
The 2013 KTM X-Bow R offers surgical precision in corners, allowing for later braking and earlier power application.
Launch & Braking Efficiency
Off the line, the 2013 KTM X-Bow R uses its 7.8 launch rating to grip and go, while the 2020 Toyota GR Supra 3.0 relies on its 7.2 rating.
Braking from high speeds is equally critical; the 2013 KTM X-Bow R stops with a score of 8.4, while the 2020 Toyota GR Supra 3.0 records 7.1.
🏁 Race Scenario Breakdown
Higher top speed rating allows for sustained high-velocity overtaking.
Superior braking and handling allow for more aggressive entry and exit speeds.
Suspension travel and tire compound optimization for loose surfaces.