Text Similarity Checker

There's no universal threshold—context matters. In academic writing, over 15-20% similarity to a source might warrant investigation. Common phrases and technical terminology naturally create some similarity. Focus on contiguous matching phrases rather than overall percentage alone.

Jaccard treats text as word sets and measures overlap. Cosine similarity considers word frequency, weighting common words more heavily. Cosine often gives higher scores for semantically similar texts. Jaccard is simpler and works well for set comparisons.

Each algorithm measures different aspects. Levenshtein counts edits (good for typo detection). Jaccard measures vocabulary overlap (good for topic similarity). Cosine measures frequency patterns (good for document classification). Choose based on what "similarity" means for your use case.

Our tool compares two texts at a time. For multiple comparisons, check each pair separately. We provide the similarity matrix concept—showing how each text relates to others—for batch analysis in our advanced tools.

It depends on the algorithm. Jaccard and basic cosine similarity ignore order (same words in different arrangements score identically). Levenshtein and sequence-based methods consider order. N-gram based methods capture partial order information.