Lot 49454806 · ON - COOKSTOWN

2015 MERCEDES-BENZ GLA GLA250 4-MATIC

VIN WDCTG4GB2FJ080468

Current bid
C$500
≈ €338

The vehicle

  • Year 2015
  • Make / model MERCEDES-BENZ GLA
  • Trim level GLA250 4-MATIC
  • Body type Sport Utility Vehicle [SUV]/Multipurpose Vehicle [MPV]
  • Condition PERMIT NO BRAND - UNFIT
  • VIN WDCTG4GB2FJ080468
  • Drive type 4WD/4-Wheel Drive/4x4
  • Horsepower 208 к.с.
  • Engine GLA250 4Matic ['15-'20] (2.0-liter turbocharged GDI M270 I4)
  • Exterior color white
  • Odometer 142,639 mi (229,555 km)
  • Listing valid until 2026-07-21 17:00
Notes
Този Mercedes-Benz GLA от 2015 г. е практичен и стилен избор за всеки ден. Умерени щети по предния калник и броня. 🚗 2015 Mercedes-Benz GLA джип 📋 Основни характеристики: пробег: 229 555 км ℹ️ Допълнителна информация: каросерия: джип цвят: бял
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Damage assessment

Body zones

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Underbody
Condition assessment 1 AI-written variant

gpt-5.6-luna

The vehicle is a 2015 Mercedes-Benz GLA. The auction records list the condition as “PERMIT NO BRAND - UNFIT” and classify the primary damage as “SIDE.” According to the Stage 1 photo review, the visible damage consists of localized front-side fender and bumper damage, along with rear bumper scuffing. The photos do not clearly support a primary SIDE damage classification, and the “PERMIT NO BRAND - UNFIT” condition claim cannot be confirmed from the visible exterior evidence. The front fender has moderate deformation, including a pronounced crease above and behind the wheel arch. The front bumper corner and adjacent fender show dark abrasion marks and scraped paint, while the black front wheel-arch cladding has light-colored scrape marks along its front section. The rear bumper and chrome-colored trim have minor surface scuffing and dark abrasion marks. Inside the vehicle, the outer bolster of the front driver’s seat has a moderate tear, with the lighter-colored underlying material exposed. A physical inspection is recommended to assess any concealed damage, verify the vehicle’s mechanical condition, and determine the repairs required.

Generated from auction photos by AI vision, then written up by an AI model. Findings are qualitative, not a certified inspection.