Against Late Fall Hardwoods, Mossy Wood scores 46/100 (), while Kade scores 64/100 ().
Based on color alignment, breakup scale, and texture density, the AI sees an approximate 18-point lean toward Kade in this particular environment.
Bassdash Mossy Wood and Tekari Kade are both mixed-scale patterns, so they behave similarly from a scale point of view. Bassdash Mossy Wood balances micro and macro elements, while Tekari Kade leans toward larger, macro-scale blocks, which shifts how each holds up in close cover versus more open sightlines. Density differs slightly: Bassdash Mossy Wood runs a bit more open and sparse, while Tekari Kade stays fairly balanced in texture, changing how much the natural background shows through. Tekari Kade carries a wider spread in scale elements, which can help it stay effective both up close and as animals get farther out.
Bassdash Mossy Wood vs Tekari Kade
Bassdash Mossy Wood and Tekari Kade 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. Bassdash Mossy Wood runs a bit more open and sparse, while Tekari Kade stays fairly balanced in texture. Hunters who prefer more background showing may favor the more open one; dense patterns can help disrupt shape in chaotic vegetation. Edge style diverges: Bassdash Mossy Wood mixes both hard and soft edges, while Tekari Kade leans into smoother, blended transitions. Softer edges often melt better into natural backgrounds, while harder edges can create stronger breakup in certain lighting. Tekari Kade's numeric scale index runs slightly higher, nudging it a bit more toward macro breakup, while Bassdash Mossy Wood stays finer on average. Bassdash Mossy Wood runs a little denser on our readings, while Tekari Kade leaves slightly more background showing through — which some hunters prefer in simpler, more open environments. Tekari Kade 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.