Against Late Fall Hardwoods, XRC scores 40/100 (), while Approach GT scores 71/100 ().
Based on color alignment, breakup scale, and texture density, the AI sees an approximate 31-point lean toward Approach GT in this particular environment.
Tekari XRC and Badlands Approach GT 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. Badlands Approach GT carries a wider spread in scale elements, which can help it stay effective both up close and as animals get farther out.
Tekari XRC vs Badlands Approach GT
Tekari XRC and Badlands Approach GT 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 leans into smoother, blended transitions, which affects how smoothly (or abruptly) each pattern merges with real brush, trunks, and rocks. Badlands Approach GT's numeric scale index runs slightly higher, nudging it a bit more toward macro breakup, while Tekari XRC stays finer on average. Badlands Approach GT lands slightly higher on the density index, adding a bit more visual texture. That can help in chaotic or brushy terrain where extra breakup is useful. Badlands Approach GT also shows a higher spread index, suggesting it can maintain its breakup across a slightly broader range of shot distances. 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.