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Sparse labeling, genetically targeting a small subset of densely distributed cells, provides a powerful approach to investigate cellular morphology, connectivity, dynamics, and functions, especially in neuroscience. However, current sparse labeling methods are generally restricted to fixed labeling densities and often exhibit a decline in sparsity over time. In this study, by maximizing the utility of commonly used recombinases and the relational recognition sites, we constructed a versatile sparse labeling system, termed Tri\u2010M (Multi\u2010recombinase, Multi\u2010recognition site, and Multi\u2010nested), built upon competitive recombination. The Tri\u2010M system, which integrates transgenic mice with viral injection, enables tunable and graded sparse labeling of specific cell types within specific brain regions with long\u2010term stability. 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