Against Late Fall Hardwoods, Cedar scores 44/100 (), while Vayel scores 54/100 ().
Based on color alignment, breakup scale, and texture density, the AI sees an approximate 10-point lean toward Vayel in this particular environment.
CeDeer Cedar and Wonrate Gear Vayel are both mixed-scale patterns, so they behave similarly from a scale point of view. Both patterns balances micro and macro elements, keeping them fairly steady across different shot distances. They are also similar in overall density, so neither one is dramatically busier or more open. CeDeer Cedar holds a slightly broader scale spread, giving it a bit more range in tight brush and mid-distance openings.
CeDeer Cedar vs Wonrate Gear Vayel
CeDeer Cedar and Wonrate Gear Vayel have been analyzed using our CamoMatrix AI engine, which measures scale, density, and edge behavior directly from the flat pattern artwork. Both land in the mixed-scale category, meaning they balance fine texture with larger breakup blocks instead of living at one extreme. Density is similar, so neither pattern overwhelms the eye or leaves too much empty space. Edge work is alike as well — both uses sharper, harder transitions, which affects how smoothly (or abruptly) each pattern merges with real brush, trunks, and rocks. CeDeer Cedar's scale index trends a touch higher, making its breakup blocks slightly larger than those in Wonrate Gear Vayel. CeDeer Cedar runs a little denser on our readings, while Wonrate Gear Vayel leaves slightly more background showing through — which some hunters prefer in simpler, more open environments. CeDeer Cedar carries more spread in our readings, which can make it more forgiving when moving between close-cover stands and semi-open edges. As always, these results come from flat pattern imagery. Real-world performance depends heavily on terrain, season, and how the garments fit and move.
This is a pattern-only comparison from flat artwork. Terrain, season, and real backgrounds will still push one or the other ahead in specific setups.
Learn how the CamoMatrix AI evaluates camouflage patterns
Defines the dominant size of shapes in the pattern.
Indicates which scale range the pattern leans toward overall.
How busy the pattern is with shapes and noise.
How hard or soft shape boundaries are.