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Samsung's Exynos smartphone share continues a slow recovery

Samsung's Exynos mobile-chip business is showing signs of recovery after several difficult years. Recent market data places Exynos at roughly 9 percent sha...

By Gameforce Mobile News Desk

Samsung's Exynos smartphone share continues a slow recovery - Gameforce Mobile news hero image

Key facts

Topic:
Mobile Industry
Published:
2026-09-15
Reported by:
Gameforce Mobile News Desk

Key takeaways

  • Samsung's Exynos mobile-chip business is showing signs of recovery after several difficult years.
  • Recent market data places Exynos at roughly 9 percent share in the second quarter of 2026, up from around 5 percent in early 2024.
  • The newly launched Galaxy S26 FE in India uses Exynos 2500, giving Samsung another high-volume product through which to demonstrate that its internal silicon can compete effectively in real-world battery life, gaming and AI workloads.

Samsung's Exynos mobile-chip business is showing signs of recovery after several difficult years. Recent market data places Exynos at roughly 9 percent share in the second quarter of 2026, up from around 5 percent in early 2024. Samsung has gradually returned its own silicon to more premium Galaxy devices while continuing to use Qualcomm and MediaTek chips across other models.

The recovery matters because Samsung is unusual among major smartphone manufacturers in having substantial internal capabilities spanning displays, memory, chip fabrication and processor design. A competitive Exynos platform reduces dependence on external suppliers and gives Samsung more control over costs and hardware integration. At the same time, consumers have long compared Exynos variants directly with Snapdragon versions of similar Galaxy phones, making performance and efficiency parity essential.

The newly launched Galaxy S26 FE in India uses Exynos 2500, giving Samsung another high-volume product through which to demonstrate that its internal silicon can compete effectively in real-world battery life, gaming and AI workloads.

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