1992 Honda NSX-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 1992 Honda NSX-Ris the superior technical chassis due to its refined lateral G-force profile.
1992 Honda NSX-R
Honda
2020 Toyota GR Supra 3.0
Toyota"In a head-to-head battle, the 2020 Toyota GR Supra 3.0 edges out the 1992 Honda NSX-R primarily due to its exceptional Performance Index performance."
| 1992 Honda NSX-R | Metric | 2020 Toyota GR Supra 3.0 |
|---|---|---|
| 710 | Performance Index | 765 |
| 6.7 | Speed | 7.2 |
| 7.2 | Handling | 7 |
| 6.2 | Acceleration | 6.9 |
| 5.9 | Launch | 7.2 |
| 6.8 | Braking | 7.1 |
| 3.5 | Offroad | 3.5 |
| 168 | Top Speed (MPH) | 165 |
| 2712 | Weight (lbs) | 3400 |
| RWD | Drivetrain | RWD |
| 120,000 | Price (CR) | 55,000 |
📈 Technical Data Analysis:
Speed & Acceleration Analysis
When it comes to straight-line performance, the 1992 Honda NSX-R boasts a speed rating of 6.7, 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 1992 Honda NSX-R features a handling score of 7.2, whereas the 2020 Toyota GR Supra 3.0 manages 7.
The 1992 Honda NSX-R offers surgical precision in corners, allowing for later braking and earlier power application.
Launch & Braking Efficiency
Off the line, the 1992 Honda NSX-R uses its 5.9 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 1992 Honda NSX-R stops with a score of 6.8, 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.