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TokenPath

Token-level citations for LLM output with attention-based source attribution

Freemium
4.3 (6 reviews)

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About TokenPath

TokenPath is an API service that adds token-level attribution to AI-generated text. You send it a source document, a question, an answer, and the claims you want verified. It returns the exact character positions in the source where each claim is grounded, along with a confidence score. The granularity goes down to individual tokens rather than just pointing at a whole document or paragraph.

The technical approach is attention-based. TokenPath reads the attention patterns from the model's generation process to trace which source tokens influenced which output tokens. That's different from asking the model to self-report its sources, which is notoriously unreliable. The attribution happens post-hoc, so it works with output from any LLM without modifying how you call the model.

Performance is competitive with Anthropic's Citations API. On the LongBench-Cite benchmark, TokenPath scores 0.815 F1 compared to Anthropic's 0.812. The claim is that it runs about seven times cheaper and five to six times faster at scale. For a 20,000-token document, attribution completes in under two seconds. It scales up to 128,000 tokens for long documents.

The API is RESTful and straightforward. You POST to the attribution endpoint with your document, question, answer, and claim spans. It returns source positions and confidence for each claim. There's also a heatmap endpoint that gives you the raw token-by-token attention matrix if you want to do your own analysis or build visualizations. Authentication uses a bearer token, and there's built-in rate limiting and usage metering.

Pricing starts with a free tier of 10 million tokens on signup, no credit card required. After that, it's $1 per million attributed tokens with no minimum spend. Volume pricing and dedicated deployments are available for enterprise use. That makes it accessible for prototyping and scales reasonably for production.

The target audience is developers building AI products that need auditable sourcing. RAG systems, legal document analysis, financial research tools, compliance workflows. Anywhere you need to show exactly where an AI answer came from rather than just trusting the model. You can reach sales at sales@tokenpath.ai or support at support@tokenpath.ai.

Key Features

  • Token-level source attribution
  • Attention-based citation extraction
  • Model-agnostic post-hoc processing
  • Raw attention heatmap endpoint
  • 20k-token documents in under 2 seconds
  • Scales to 128k token context

Pros & Cons

What we like

  • Granular character-level source positions
  • Works with any LLM without model changes
  • Generous free tier of 10 million tokens
  • Cheaper and faster than comparable solutions

Room for improvement

  • API only, no self-hosted option
  • Requires structured claim extraction beforehand
  • Younger product with smaller adoption
  • No SDK, requires direct HTTP calls

Frequently Asked Questions

What is TokenPath?
TokenPath is an API that traces AI-generated claims back to exact positions in source documents. It uses attention patterns to identify which source tokens influenced the output, giving you token-level citations with confidence scores.
Does TokenPath work with any LLM?
Yes. Attribution happens post-hoc, so you can use TokenPath with output from any model. You don't need to modify how you call your LLM or use a specific provider.
Is TokenPath free?
It's freemium. You get 10 million tokens free on signup with no credit card required. After that, it's $1 per million attributed tokens. Enterprise volume pricing is available.
How accurate is TokenPath?
On the LongBench-Cite benchmark, TokenPath scores 0.815 F1, matching Anthropic's Citations API at 0.812. The attention-based approach avoids the unreliability of asking models to self-report their sources.

Best For

Adding verifiable citations to RAG responsesAuditing AI-generated legal or financial analysisBuilding transparency into chatbot answersDebugging hallucination in retrieval pipelines

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Reviews (6)

P
Priya Larsen Verified

Quietly excellent

Three months of TokenPath later, here is what holds up. Their take on raw attention heatmap endpoint is genuinely good.

5/5/2026 14 found this helpful
H
Hassan Esposito

It just works

TokenPath solves a real problem for me without making a fuss about it. Setup was painless and I was productive the same day. It does what it says, which is rarer than it should be. Found it works best for adding verifiable citations to rag responses. Would sign up again without thinking twice.

7/9/2026 13 found this helpful
S
Sana Clark

Finally something that fits

Picked TokenPath for the price, stayed for the quality. The core workflow is smooth once you are set up. Found it works best for debugging hallucination in retrieval pipelines. Recommending it to people in a similar spot.

4/13/2026 10 found this helpful
L
Leon Silva Verified

Exactly what I needed

TokenPath has quietly become part of my daily flow. The cheaper and faster than comparable solutions is more useful than I expected. Recommending it to people in a similar spot.

6/13/2026 8 found this helpful
D
Daiki Petrov Verified

Does the job, a few gripes

Found TokenPath on a Show HN thread and I am glad I clicked. Their take on attention-based citation extraction is genuinely good. It just works, day after day, without surprises. My only gripe is api only, no self-hosted option. It earns its place in my stack.

6/22/2026 5 found this helpful
O
Obinna Ferrari

Two months in, no regrets

Hadn't planned on switching, but TokenPath was hard to ignore. The defaults are sensible, so I was not fighting settings on day one. It slotted into my routine without much fuss. Easy yes for anyone weighing the same trade offs.

5/25/2026 3 found this helpful