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Paste two texts, get a 0-100% similarity score. Compares meaning, not word overlap.
Semantic similarity measures how close two pieces of text are in meaning, rather than how many words they have in common. Two sentences can share almost no words and still mean the same thing - and this calculator is built to catch exactly that.
Plagiarism checkers and text-compare (diff) tools look at literal word overlap. This semantic similarity calculator compares meaning using AI embeddings, so a paraphrase with completely different wording can still score as highly similar. That makes it useful for comparing paraphrases, deduplicating content, matching questions to answers, evaluating model outputs, and checking whether two passages say the same thing.
Each text is converted into an embedding vector using OpenAI's
text-embedding-3-small model. We then take the
cosine similarity between the two vectors and rescale it to an
intuitive 0-100% score - higher means closer in meaning.
TypeSafe's System One decision model. Give it a state and typed questions; it answers with probabilities, not prose. Free to try while the sponsored pool lasts.
Held in this tab's memory only, never stored, sent to this server as X-TypeSafe-Key and
forwarded once to api.typesafe.ai. Runs with your key skip the sponsored pool.
Jev is TypeSafe AI's System One model: a decision model rather than a chat model. You hand it a state (free text or a JSON object) and one or more typed questions, and it returns a probability for each answer instead of a paragraph. That makes the output something code can branch on directly.
noul is a yes/no question and comes back as a single probability.
choice picks one of up to 255 named options and returns the full distribution plus a
confidence. score rates the state on a scale of 2 to 10 described levels and returns
the expected level with the distribution behind it. Every question can carry short criteria that
pin down what the answers mean.