K&N Motorcycle Oil Filter: High Performance, Premium for Suzuki Motorcycles

K&N oil filters developed for powersports vehicles feature an advanced synthetic-blend filtration media that is highly efficient and compatible with a variety of oils, including synthetic, petroleum-based, and blended oils. These filters are even suitable for high concentrations of synthetic racing oils. The heavy-duty canister is designed to be exceptionally durable, while a 17mm welded hex nut allows for quick and easy removal of the filter.

These filters are engineered to support high flow rates, providing excellent protection against harmful engine contaminants. Each filter is specifically designed to meet the service intervals recommended by vehicle manufacturers and is backed by K&N's 1-Year Limited Warranty. The filters are compatible with a wide range of vehicles, including the following models:

• 2002-2018 SUZUKI AN650 Burgman
• 2013-2018 SUZUKI AN650A Burgman ABS
• 2006-2018 SUZUKI AN650A Burgman Executive
• Many more models...

Key features of these oil filters include their ability to provide exceptional flow rates, ensuring a consistent flow of oil to the engine. The synthetic-blend filtration media effectively removes most harmful contaminants, offering superior protection for the engine. These filters have undergone extensive laboratory testing to confirm their exceptional capacity and burst strength.

In addition to their high performance and durability, these filters are also easy to remove with their Wrench-Off design. The heavy-duty construction of the canister ensures long-lasting reliability. To provide customers with peace of mind, K&N offers a 1-Year Limited Warranty on these oil filters, guaranteeing their quality and performance.

Designed to be vehicle-specific, these filters offer versatile compatibility with all synthetic, conventional, and blended motor oils. Whether you're using synthetic racing oils or regular petroleum-based oils, K&N oil filters are a reliable choice for protecting your engine and ensuring optimal performance.

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