Ethereum Verification Benchmark

Evaluating AI deductive reasoning for Smart Contracts.

We evaluate frontier models on fresh problems derived from production Ethereum contracts. Each task pairs a Verity contract model with a fixed formal specification. A model succeeds only if it produces a proof accepted by Lean.

Leaderboard

#ModelPass rate
1OpenAIgpt-5.6-sol
34.0%
2Kimikimi-k3
28.0%
3Metamuse-spark-1.2
20.0%
4OpenAIgpt-5.6-terra
20.0%
5OpenAIgpt-5.6-luna
18.0%
6MiniMaxminimax-m3
14.0%
7Z.aiglm-5.3
14.0%

Pass rate is the share of proofs that compiled against the fixed specification. Each cohort has 50 terminal verdicts on the same STRAT-50 panel under the p4_normal budget of 16 attempts and 120 tool calls; infrastructure-invalid attempts are excluded. The table lists the highest-scoring completed cohort per model family, out of 12 completed cohorts.

Scaling Law

We re-ran MiniMax M3 on 50 tasks solved by the frontier model with steadily larger effort budgets. Its solve rate climbs from 12% to 96%, approaching a near-perfect rate as effort grows.

010203040507.9M15.9M23.8M31.7M39.7Msolved taskscumulative total tokens
Estimation: S(E) = 1 / (1 + (4.6M / E)1.19)

About the benchmark

The benchmark was developed with support from the Ethereum Foundation as part of our work to make formal verification of smart contracts more practical. Instead of asking a model to explain whether a contract is safe, Verity asks it to produce a mathematical proof that a specific formal property follows from a specific contract model and specification.

Version 0.2 contains 240 proof tasks from 37 deployed or ecosystem-relevant Ethereum cases. Each task includes a reference proof and a frozen grading setup. The model may attempt the proof, but it may not change the theorem statement or specification.

The leaderboard scores STRAT-50, a deterministic 50-task panel drawn from that manifest. It reports the observed solve rate on the panel, not an unweighted estimate of full 240-task performance.

A proof is considered correct only if it is accepted by the Lean 4 kernel, with no additional assumptions. Version 0.2 is pinned to Lean 4.24.0.

The frozen manifest, per-cohort run bundles, and SHA-256 checksums for this version are published in the v0.2 release.

Paper & methodologyPreprint, dataset, and grading rulesView source on GitHubTasks, specs & grading harness

Every task, specification, and grading harness is open source. Results are fully reproducible.

In collaboration with

Ethereum Foundation grantee
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