TokenPath provides token-level attribution for AI model answers, resolving each claim to the exact source span in the document.
It works with any model's output without changing the stack, requiring only a single API call after the answer is generated.
Three citation methods are compared: ask model (no granularity), retrieve and re-rank (cannot disambiguate identical values), and read attention (granular, unambiguous, and fast).
On LongBench-Cite, TokenPath's read-attention method achieves 0.815 F1, matching Anthropic's Citations API at about 7× lower cost and 5–6× faster speed.
The API accepts a document, question, answer, and claim spans, returning a source span and confidence score for each claim.
Pricing is $1 per million tokens with 10 million free tokens upon signup, and no monthly minimum.
TokenPath is designed for the agentic era, enabling auditing of what agents actually used by reading attention from the model.