Distance Metrics Calculator
Compare different distance and similarity metrics between two vectors.
Results
Euclidean Distance
L2 norm: straight-line distance
Manhattan Distance
L1 norm: grid-based distance
Cosine Similarity
Angular similarity (-1 to 1)
Cosine Distance
1 - cosine similarity (0 to 2)
Dot Product
Inner product of vectors
Chebyshev Distance
L∞ norm: maximum difference
When to Use Each Metric
Euclidean: General-purpose, when actual distance matters. Good for clustering.
Manhattan: When movement is grid-based. Robust to outliers.
Cosine: When direction matters more than magnitude. Best for text/embeddings.
Dot Product: Fast similarity check. Often used with normalized vectors.
Chebyshev: When only the maximum deviation matters. Gaming (king movement).