2013 KTM X-Bow R vs 2024 Hyundai Ioniq 5 N
AI Telemetry Verdict:In this head-to-head, the 2024 Hyundai Ioniq 5 Nholds the statistical edge in Performance Index (822). 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
2024 Hyundai Ioniq 5 N
Hyundai"Analyzing the raw telemetry, the 2024 Hyundai Ioniq 5 N proves to be the more capable machine in all-around festival racing, outclassing the 2013 KTM X-Bow R."
| 2013 KTM X-Bow R | Metric | 2024 Hyundai Ioniq 5 N |
|---|---|---|
| 765 | Performance Index | 822 |
| 6.1 | Speed | 7.2 |
| 8.2 | Handling | 7.5 |
| 8 | Acceleration | 8.8 |
| 7.8 | Launch | 9.2 |
| 8.4 | Braking | 7.9 |
| 2.2 | Offroad | 4.8 |
| 155 | Top Speed (MPH) | 162 |
| 1742 | Weight (lbs) | 4850 |
| RWD | Drivetrain | AWD |
| 90,000 | Price (CR) | 66,000 |
📈 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 2024 Hyundai Ioniq 5 N hits 7.2.
The 2024 Hyundai Ioniq 5 N 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 2024 Hyundai Ioniq 5 N manages 7.5.
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 2024 Hyundai Ioniq 5 N relies on its 9.2 rating.
Braking from high speeds is equally critical; the 2013 KTM X-Bow R stops with a score of 8.4, while the 2024 Hyundai Ioniq 5 N records 7.9.
🏁 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.