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A defensible blockchain-throughput benchmark reports more than a large transactions-per-second number. It defines exactly what counts as a transaction, specifies the workload, identifies where measurement starts and ends, records the chain and client environment, and reports successful sustained throughput together with latency and a named finality endpoint.
That discipline prevents the most common error in blockchain comparisons: presenting a theoretical ceiling, a CPU-only execution test, an inclusion count, and finalized production transactions as though they were the same metric. The method below gives you a repeatable way to design, run, analyze, and publish a benchmark that readers can actually compare.
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What a throughput benchmark should answer
Throughput is not a property of a protocol in isolation. It is the amount of specified work a particular implementation can complete successfully under stated conditions. A useful headline therefore looks like this: “X successful transactions per second, sustained for Y minutes, using workload Z, measured from endpoint A to endpoint B, on client/version C and hardware D.”
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| Result type | What it measures | What it does not prove |
|---|---|---|
| Theoretical capacity | A calculation from assumptions such as bandwidth, message size, block limits, or an idealized schedule. | That a live network can sustain the number with real execution, propagation, failures, or finality. |
| Synthetic execution benchmark | Execution cost for a controlled operation or contract workload, often on one machine or a set of clients. | End-to-end network throughput or production user experience. |
| End-to-end testnet benchmark | Transactions generated, propagated, executed, included, and possibly finalized during a controlled run. | Performance under every production workload, geographic condition, or fault pattern. |
| Production observation | Activity and confirmation behavior on a live network during a defined time window. | Capacity under a different workload or at a different utilization level. |
A benchmark can legitimately report several of these results, but label each one. Never turn one category into another by shortening the description to “TPS.”
Define what counts as a transaction
The first page of a benchmark report should contain a transaction specification. Two operations that both increment a counter may have very different costs if one touches a single account and the other reads and writes many storage slots.
Describe transaction semantics
- Operation type: native transfer, contract call, token operation, vote, message, or another protocol-specific action.
- Payload: message size, calldata length, signatures, and any attached value.
- State access: number and pattern of reads and writes, account creation, storage growth, and whether accesses are hot (reused) or cold (new).
- Contract complexity: opcodes, precompiles, loops, cryptographic work, and worst-case branches exercised.
- Contention: whether many transactions update the same account, nonce sequence, order book, or state key. Contention can serialize work that appears parallel on paper.
- Workload origin: synthetic generation, replay of recorded traffic, or an application-derived trace. State which one was used and why.
If the test isolates one operation, say so plainly. An operation-isolating test answers a narrow execution question; it does not establish whole-network TPS.
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Count a transaction as successful only at the endpoint you name. Keep separate counters for submitted, accepted by a node, included, executed without a protocol-level failure, finalized, rejected, expired, reverted, and still pending. A reverted contract call may consume resources and appear in a block, but it is not equivalent to a successful application operation. Publish both the numerator and denominator so readers can see whether a high rate was achieved by allowing a large backlog or failure rate.
Set the measurement boundary and layer
“Latency” and “throughput” change meaning when the boundary changes. Mark the start and end timestamps in your instrumentation and use the same boundary for every system in a comparison.
Possible latency endpoints
- Submission latency: time from the generator sending a request until a node accepts it.
- Inclusion latency: time from submission until the transaction appears in a block or batch.
- Confirmation latency: time until your chosen confirmation rule, such as a depth or checkpoint condition.
- Protocol-finality latency: time until the protocol’s finality mechanism makes the state irreversible under its stated assumptions.
Do not report one average that mixes these endpoints. Give a distribution—at least median (p50) and tail percentiles such as p95 and p99—because users experience the tail, not the average alone. Include the exact confirmation or finality rule in the methods section.
Separate execution, propagation, and consensus
A local execution test can show how quickly a client processes an already available block. It says little about transaction propagation, block production, validator communication, or finality. An end-to-end test must include those stages and identify where time is spent. If you report a local figure, call it local execution throughput rather than network TPS.
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Keep layer-1 and layer-2 activity separate
Rollups execute user transactions offchain and post compressed batches to a layer-1 chain. The user-transaction rate and the layer-1 settlement rate answer different questions. Report both when possible: user operations per second at the rollup boundary, and batches or settlement transactions per second at layer 1. Explain the batching interval, compression, proof or challenge process, and what users must trust.
Data availability is part of this comparison. Users and independent verifiers need access to the data required to check proposed state changes. A high user TPS number with a different data-availability model is not directly equivalent to an L1 rate that publishes all transaction data.
Build a repeatable benchmark, step by step
1. State the hypothesis and scope
Write one sentence describing what you are testing: for example, “How many finalized contract calls per second can client X sustain under a fixed state-access pattern?” Name the chain, layer, client implementation and version, protocol release, and whether the goal is capacity, latency under load, or resource efficiency. Do not combine unrelated questions in one run.
2. Specify the workload before generating it
Create a workload document that another team could implement without guessing. Include transaction fields, account population, nonce strategy, state size, hot-spot percentage, contract bytecode or call path, payload distribution, and the ratio of each transaction type. Record whether the initial state is empty, pre-populated, or restored from a snapshot. If you replay production traffic, document anonymization and any transformations.
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Measure both capacity and behavior under realistic demand. Use a ramp from idle to saturation, a steady period at each level, and a burst profile if the application experiences spikes. Record the offered rate, not just the rate that succeeded. Include a warm-up period so caches, just-in-time compilation, database files, and peer connections reach stable conditions. State the duration of every measurement window and whether warm-up samples were discarded.
4. Record the complete environment
- CPU model, core count, clock behavior, memory capacity, storage type and free space.
- Operating system and kernel, virtualization or container settings, and resource limits.
- Client name and exact version, build flags, database settings, cache sizes, and consensus configuration.
- Network topology, peer count, bandwidth, latency, packet loss, geographic placement, and whether peers were dedicated or shared.
- Generator hardware and software, clock synchronization method, and how transactions were distributed among sending nodes.
- Chain state size, validator or sequencer role, and any pruning, archive, or snapshot mode.
A result without these details is difficult to reproduce and easy to misinterpret. If a value is unknown, mark it as unknown rather than silently assuming a default.
5. Instrument every stage
Use synchronized clocks and a transaction identifier that follows each request through submission, acceptance, inclusion, execution outcome, confirmation, and finality. Capture block or batch timestamps, block sizes, gas or equivalent resource consumption, queue depth, dropped requests, retries, and node errors. Monitor CPU, memory, storage I/O, network bandwidth, and temperature or throttling indicators. Keep raw logs so aggregate figures can be audited.
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6. Run repeated trials
Run multiple independent trials for every load level. Reset the state or document exactly how state carries over between trials. Randomize transaction order when the workload permits, and use a fixed seed when deterministic replay is needed. Report the number of runs, central tendency, and variation; a single best run is not a benchmark.
7. Calculate the right rates
For a measurement window, calculate successful throughput as successful transactions reaching the stated endpoint ÷ measured seconds. Also publish offered throughput, failure rate, pending backlog at the end of the window, and the interval over which each rate was calculated. For block-based systems, show per-block counts and the block interval instead of assuming a constant interval. Include confidence intervals or run-to-run ranges when the sample size supports them.
8. Publish enough material to reproduce the run
Release the workload definition, generator version, configuration files, client build identifiers, measurement scripts, raw or aggregated logs, and a description of how outliers were handled. A reproducible benchmark does not require identical hardware, but it does require enough information to explain differences.
Metrics to report beside TPS
| Metric | Recommended reporting | Why it matters |
|---|---|---|
| Successful sustained throughput | Transactions per second at the named endpoint, over a stated steady window. | Shows useful work completed rather than requests merely sent. |
| Offered load | Generator rate for each test phase. | Reveals whether the system was lightly loaded, saturated, or overwhelmed. |
| Latency distribution | p50, p95, p99, maximum, and sample count for submission, inclusion, confirmation, or finality. | Exposes queueing and tail behavior hidden by an average. |
| Finality | Named rule, time to that rule, and percentage reaching it during the window. | Distinguishes visible inclusion from irreversible state. |
| Failure and backlog | Rejected, reverted, expired, dropped, and pending counts. | Prevents a high apparent rate created by discarding work. |
| Resource use | CPU, memory, storage I/O, bandwidth, and peak values at each load. | Shows the hardware cost and bottleneck behind the rate. |
| Block or batch properties | Interval, size, resource units, transaction count, and propagation time. | Explains batching effects and burst capacity. |
Ethereum-specific context you must label
Ethereum’s documentation describes blocks as created and committed about once every twelve seconds. That is a protocol description, not a throughput benchmark. A block interval tells you how often batches arrive; it does not tell you how many transactions each batch contains, how much execution they require, or how long they take to finalize.
Ethereum proof-of-stake finality uses supermajority links between checkpoints. Ethereum’s roadmap page, updated July 23, 2026, describes finality as taking about 15 minutes. Treat that figure as a dated, Ethereum-specific description of its documented process—not as a universal latency threshold and not as a substitute for measuring your workload’s finality distribution.
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What EIP-7904 demonstrates—and what it does not
Ethereum EIP-7904 is a useful example of a bounded empirical method. It creates synthetic blocks to isolate EVM operations, varies execution counts and operation parameters, repeats tests to account for timing variability, and uses Benchmarkoor to collect block execution time and operation counts. The latest run described measures Besu, Erigon, ethrex, Geth, Nethermind, and Reth.
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The proposal uses 100 million gas per second as the target for its compute-cost analysis and reports that every operation and precompile it examined performed at or above that target under the current gas schedule. That number belongs to the EIP’s synthetic operation benchmark. The authors explicitly scope the work to computational complexity measured as execution time on a bare CPU and exclude network-related costs such as state persistence. It must not be rewritten as live-chain transactions per second.
This is the correct way to quote a specialized result: identify the workload, clients, repetitions, target, and excluded costs in the same paragraph. Readers can then use it to reason about execution limits without mistaking it for propagation, consensus, or finalized user throughput.
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The Solana 2018 whitepaper gives a theoretical bandwidth calculation based on assumptions including a 1 gigabit-per-second link and a 176-byte transaction message. The arithmetic can illustrate an upper bound under those assumptions, but it is not a comparable end-to-end measurement. It does not, by itself, account for signature verification, state access, execution, retransmission, validator communication, failures, or finality.
Whenever you publish a theoretical number, put the assumptions beside it and label the result as modelled capacity. A reader should never have to infer whether a figure came from a whitepaper equation, a saturated synthetic test, or live production telemetry.
How to compare two chains fairly
Align the dimensions in this table before drawing a cross-chain conclusion:
| Dimension | Alignment question | If it cannot be aligned |
|---|---|---|
| Transaction semantics | Do both tests perform equivalent state changes, payload sizes, and contract work? | Report separate workloads; do not merge the rates. |
| Success criterion | Are you counting accepted, included, executed, confirmed, or finalized operations? | Publish each endpoint separately. |
| Layer | Are both figures L1 transactions, L2 user operations, or one of each? | Show user and settlement rates in separate columns. |
| Time window | Are both sustained over comparable durations, with warm-up treated the same way? | State the mismatch and avoid a direct ranking. |
| Load | Were both systems driven to saturation or tested at the same offered rate? | Describe one as a capacity test and the other as a latency-under-load test. |
| Environment | Are hardware, client versions, topology, and configuration documented? | Qualify the result as environment-specific. |
| Security and availability | Do the systems provide comparable data availability and finality assumptions? | Present the performance difference together with those assumptions. |
Compare sustainable successful throughput first, then latency distribution, finality semantics, resource requirements, and security or data-availability assumptions. A higher number is not automatically better if it requires materially different hardware or offers a different verification model.
Layer-2 reporting that readers can interpret
For a rollup or other layer-2 system, publish at least three rates when the data is available:
- User-operation throughput: successful operations measured at the layer-2 submission or execution endpoint.
- Batch throughput: batches or compressed data posted to the settlement layer per second or per settlement interval.
- Settlement finality: time until the layer-1 state containing the batch reaches the stated finality rule.
Also report batch size, compression or aggregation method, posting interval, proof or challenge timing, and the data-availability mechanism. Without these details, “L2 TPS” can describe a high offchain execution rate while omitting the cadence and guarantees of settlement.
A publication-ready result format
Use a compact methods box before the charts. It should contain:
- Chain, layer, network, protocol release, client and exact version.
- Transaction definition, workload mix, state-access pattern, and workload source.
- Generator rate, warm-up duration, steady-state duration, number of trials, and reset procedure.
- Hardware, operating system, storage, configuration, topology, geography, and bandwidth.
- Measurement start and end points, success definition, finality rule, and clock method.
- Successful throughput, offered load, latency percentiles, finality time, failure counts, backlog, and resource usage.
- Known exclusions, such as network propagation, state persistence, proof generation, or data availability.
Charts should show throughput and latency against offered load, with error bars or run ranges. A separate chart for resource use helps readers see whether a rate is sustainable on the stated machine. Include raw per-run values in an appendix or data file rather than publishing only a rounded headline.
Common benchmark failures and fixes
Counting submissions as completed transactions
Symptom: the generator reports a large TPS while the node has a growing pending queue. Fix: calculate throughput from transactions that reached the declared endpoint and publish backlog and drop counts.
Using a single average latency
Symptom: the mean looks acceptable while a minority of users wait far longer. Fix: report p50, p95, p99, maximum, and the sample count for each endpoint.
Calling a CPU test network TPS
Symptom: an operation benchmark is presented as a live-chain rate. Fix: state that it measures local execution, list excluded network costs, and reserve “TPS” for the end-to-end result.
Comparing unlike transaction types
Symptom: one chain runs simple transfers while another runs storage-heavy contracts. Fix: use matched workloads or publish separate workload classes with their own rates.
Ignoring warm-up and repetition
Symptom: the best run is reported from a cold or unusually favorable state. Fix: define warm-up, run repeated trials, reset or document state, and report variation.
Mixing L1 settlement and L2 user activity
Symptom: a rollup’s user-operation rate is compared directly with an L1 block rate. Fix: show user, batch, and settlement-finality metrics separately.
Leaving out resource and security context
Symptom: a high rate cannot be reproduced or depends on a materially different availability model. Fix: publish machine requirements, topology, data-availability assumptions, and the verification or finality model alongside performance.
Quick Recap
Checklist for a defensible benchmark
- Transaction semantics and workload source are explicit.
- Offered load, warm-up, duration, repetitions, and reset procedure are recorded.
- Success, failure, pending, inclusion, and finality states are counted separately.
- Measurement boundaries are timestamped and consistent.
- Latency distributions accompany successful sustained throughput.
- Chain, layer, client/version, hardware, configuration, and network conditions are disclosed.
- Synthetic, theoretical, local-execution, and end-to-end results are labelled distinctly.
- L1 settlement, L2 user activity, and data availability are not conflated.
- Resource consumption and relevant security assumptions are reported.
- Raw data, scripts, and configuration are available or the limitations are explained.
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