Search Results

Gas metering on EVM-compatible blockchains assumes that execution conditions are stable: that the resource mix is constant enough to justify collapsing execution costs into a single scalar with fixed relative prices, and that state drift between submission and execution time does not materially alter a transaction’s outcome. We measure the extent to which this assumption fails. We present a trace-level measurement study of EVM workloads on Ethereum (L1) and Base (L2) throughout 2025, sampling 3{,}000 blocks per day per chain. We decompose each transaction into opcode-level execution gas, intrinsic gas, refunds, and persistent state deltas including storage slots, contract bytecode, and account state. To measure state sensitivity, we re-execute transactions sampled during September 2025 on progressively older blockchain states and record how gas usage, execution outcomes, and storage access patterns change. We find the resource mix to be far from stable: on Base, storage reads and compute account for 29.2% and 24.3% of execution gas, while Ethereum devotes 34.9% to storage writes. The mix is not stable on the same chain either: Ethereum’s gas limit doubling during 2025 shifted its resource profile measurably toward more compute-heavy, Base-like patterns. Base also exhibits a higher fraction of cold storage reads at 49.7%, compared to 39.6% on Ethereum. Persistent state growth, a permanent cost priced as a transient one, reaches 435 GB on Base versus 30 GB on Ethereum, with different composition. We further find that execution outcomes are equally unstable: gas estimates vary across nearby historical states for 46.0% of transactions on Base, compared to 13.9% on Ethereum, with especially high sensitivity for MEV and DeFi activity. Storage access patterns also diverge across execution states, limiting the effectiveness of access lists and complicating parallel execution. Our measurements provide an empirical foundation for multi-dimensional gas metering and explicit pricing of state growth. They show that state-sensitive execution behavior complicates workload estimation and transaction parameterization, directly affecting the predictability of transactions' execution and user experience.