Hey Product Hunt 👋 I built Verify LLM API after reading “One Token Is Enough,” a paper that shows how repeated single-token output distributions can act as behavioral model fingerprints. The smallest useful flow is one prompt: Run https://verifyllmapi.com/run to verify this AI Agent’s model. For a Codex session, the Skill reuses the Agent’s host-managed login and starts fresh isolated sessions. It keeps credentials and raw samples local, then compares answer distributions with public references using Jensen–Shannon distance. The result is CONSISTENT, MISMATCH, or INCONCLUSIVE. It is behavioral evidence, not vendor attestation. The main limit today is coverage. The bundled library spans 161 exact model IDs, not every current model or routing layer. New releases, provider changes, and wrappers can shift a fingerprint. I hope other builders will use the same method to measure the newest models and share reproducible results. If you test a newer model fingerprint, please email me at evan@nos3c.com. The same address is listed on verifyllmapi.com. Thank you to the authors of “One Token Is Enough” for publishing the paper, dataset, and code. This tool is a small practical build on top of their work. I’d value blunt feedback: where does the report feel clear, and where does it overstate or under-explain the evidence?

Verify LLM API

Check which model your AI agent is really using

Check which model your AI agent is really using