Capability leaderboards rank whether a model got the task right, how fast, and how cheap. That matters. But in law, medicine, finance, and defense, a right answer you cannot prove is a liability. The Provenance Benchmark ranks AI systems on a different axis: is each decision signed, court-admissible, and offline-verifiable. On this axis, every frontier model scores zero. Only Hive signs.
The same models on every published legal-agent leaderboard, scored the way those boards score them: all-pass rate and cost per task. We add the axis none of them measure — is the decision signed, court-admissible, and provable later. Capability is ranked everywhere. Provenance is zero across the board — until Hive.
| System | LAB all-pass | Cost / task | Signed inference | Court-admissible | Provable later |
|---|---|---|---|---|---|
| Any model + Hive signing | model’s own | +$0.0000732 / call | YES | YES | YES |
| Claude Opus 4.7 | 7.1% | $50.90 | NO | NO | NO |
| Claude Sonnet 4.6 | 5.4% | — | NO | NO | NO |
| GPT-5.5 | 2.1% | — | NO | NO | NO |
| Gemini 3.5 Flash | 0.8% | — | NO | NO | NO |
| GLM 5.1 | 12 / 100 | $121 / 100 tasks | NO | NO | NO |
| Kimi K2.6 | 11 / 100 | $84 / 100 tasks | NO | NO | NO |
| DeepSeek V4 Pro | — | — | NO | NO | NO |
All-pass and cost-per-task figures are drawn from published legal-agent benchmark results (2026), reproduced only to show the axis they leave blank. The signing and verifiability columns reflect properties of the call as deployed by default, not model quality. Hive is a signing layer that sits underneath any model — it does not change a model’s task accuracy, and it makes no claim to. Capability and provenance are two different axes. This board measures the second one.
Signed inference — every call returns a cryptographic signature over the exact inputs and output. Court-admissible receipt — the signature is anchored and structured to stand as evidence (eIDAS 2.0 / ALCOA aligned). Offline-verifiable — anyone can verify the receipt without calling back to the provider. Tamper-evident — any change to inputs or output breaks verification. Hive uses real ML-DSA-65 (NIST FIPS 204), signing in 7ms with no meaningful hit to the latency budget.
Legal agentic work (eIDAS 2.0, ALCOA), healthcare and PHI, clinical and pharma (21 CFR Part 11), finance (audit and recordkeeping), defense, and the EU AI Act (Article 13 audit trail). In every one of these, the question that decides liability is not “was the model good” — it is “can you prove what it did.”
See signed inference Read the benchmarks