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The fairest measure of an AI coding agent is the cost of a verified, regression-safe task—not the number of tokens it uses. A useful evaluation records every billed token class, tool call, retry, and second of latency, then divides the total spend by tasks that actually meet a written success rubric. Repeat the same tasks across runs, report distributions rather than one result, and compare success against cost to reveal which agent variants deliver the best frontier.
What “efficient” means for an AI coding agent
An agent can be token-cheap and still be expensive in practice if it leaves failing tests, introduces regressions, or requires substantial engineer cleanup. Conversely, an agent that consumes more context and reasoning tokens may be the better choice if it completes substantially more tasks without human rework. Efficiency therefore has an outcome denominator: verified successful tasks.
Use these measurements together:
- Cost per successful task: total provider-billed spend divided by tasks that satisfy the success rubric.
- Success at a fixed budget: successful tasks divided by all tasks attempted below a stated dollar or token ceiling.
- Tokens per successful task: total tokens divided by successful tasks. You may also report the reciprocal, successful tasks per token.
- Reliability: successful runs divided by repeated runs of the same task, with a confidence interval.
- Time and operational effort: wall-clock completion time, tool-call efficiency, retry rate, and human rework.
Always state which definition you use. “Token efficiency” can mean successful tasks per token or tokens per successful task; the two metrics point in opposite directions.
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Start with a task that can be judged independently: a fixed issue, feature request, test task, or repository question-and-answer item. Write the success rubric before launching an agent. The rubric should identify required behavior, tests that must pass, acceptable files or interfaces, and any constraints such as backward compatibility or security requirements.
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Record the evaluation context
Preserve the repository commit, exact task text, permitted tools, timeout, model and agent versions, and harness version. Keep model identity, reasoning setting, context-window configuration, cache policy, and agent scaffold visible in every result. A change in any of these can alter token use or success, so results from different configurations should not be silently pooled.
Use a stable task set
Run each variant on the same task list. Do not remove difficult tasks after seeing failures unless you publish a separate, predeclared rule for broken-task handling. Keep task provenance and benchmark version so another team can reconstruct what was evaluated.
Capture complete telemetry
Instrument the model gateway, agent harness, tool runner, and evaluator as one trace. A request-level log should let you reconstruct the entire run and the spend that produced it.
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- Input tokens.
- Uncached input tokens.
- Cached input tokens.
- Cache-write tokens.
- Reasoning tokens, when the provider reports or separately bills them.
- Output tokens.
Do not convert missing telemetry to zero. Exclude an attempt from token-based aggregates when a required field is unavailable, and report how many attempts were excluded. Artificial Analysis reports token classes and pooled per-task-attempt averages; that style is useful because it keeps cache and reasoning components visible instead of hiding them inside a single total.
Operational fields
- Every tool invocation, tool name, start and end time, and exit status.
- Retries, including the reason for each retry and whether the retry was automatic or prompted by a person.
- Wall-clock start, first response, final response, and task completion times.
- Commands run, test output, exit codes, and evaluator version.
- Human interventions, files changed after the agent stopped, and minutes spent on rework.
Use a stable run identifier linking request traces, repository state, test results, and the final rubric decision. This prevents a successful patch from being credited to the wrong model call when an agent performs several retries.
Calculate provider-billed cost correctly
Apply the rates that were in force when each request ran. Include cache-read and cache-write rates when the provider bills them; do not assume cached input is free. Sum all requests in a run, including retries and tool-related model calls.
Run cost = Σ(input tokens × input rate
+ cached-input tokens × cached rate
+ cache-write tokens × cache-write rate
+ reasoning tokens × reasoning rate, if separately billed
+ output tokens × output rate)Then calculate:
Cost per successful task = total run cost ÷ number of tasks meeting the success rubricKeep currency, region, price effective date, and whether a rate is recurring or promotional in the report. If a provider changes prices during a study, apply the rate attached to each request rather than one blended rate.
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Subscriptions and API spend are different units
A consumer subscription usually charges for access over a period, while an API evaluation charges for metered requests. Do not compare them directly without a stated normalization, such as allocating the subscription fee across a defined number of completed tasks and reporting that assumption separately. A subscription’s usage limits, model mix, and fair-use rules can also change the effective cost.
Define task success and regression safety
Automated tests are necessary but not sufficient. Use tests for behavior that can be executed reliably, then have a reviewer check requirements that tests cannot capture, such as API shape, maintainability, security, or an explicit prohibition on unrelated edits.
Adopt the two-test pattern
SWE-bench provides a concrete pattern: a patch passes only when it satisfies both FAIL_TO_PASS tests for the target issue and PASS_TO_PASS tests covering unrelated behavior. The first group demonstrates that the requested defect is fixed; the second guards against regressions.
Separate outcomes instead of one vague score
- Successful: all required tests pass, regression checks pass, and the review rubric is satisfied.
- Partial: some requirements pass but the task is not shippable under the rubric.
- Failed: the target behavior remains broken, the patch cannot be applied, or required tests fail.
- Invalid: the task or evaluator is demonstrably broken under a predeclared rule.
Publish the counts for each category. Do not silently treat invalid tasks as successes or failures; either exclude them under the documented rule or show a sensitivity analysis with and without them.
Repeat runs to expose stochastic behavior
One run is a sample, not a reliability estimate. Execute every agent variant multiple times on the same task set with the same settings. Report the success rate, median and percentile cost, token distributions, latency, retry rate, and confidence intervals.
Use medians for skewed costs and latencies, and include a high percentile such as p95 to show tail behavior. Averages can be dominated by one runaway context or repeated retry loop. State the denominator for every percentage: tasks, runs, or successful tasks.
Reliability is:
Reliability = successful runs ÷ repeated runs on the same taskFor a task-level view, show how often each task succeeds across runs. An agent with a high overall rate may still be unreliable on a small group of critical tasks.
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Report the metrics readers need for a fair comparison
Keep the following axes in one scorecard. They answer different operational questions and should not be collapsed into a single leaderboard number.
| Metric | What it answers | How to report it |
|---|---|---|
| Verified task success | Does the patch meet the rubric and avoid regressions? | Successful tasks and success rate, with the test and review rule. |
| Cost per successful task | What does a shippable result cost? | Total billed run cost divided by successful tasks. |
| Token mix | Where is context and reasoning spend going? | Input, uncached input, cached input, cache-write, reasoning, and output tokens. |
| Success at a fixed budget | How often does the agent finish before a spending limit? | Successful tasks divided by attempts under the stated dollar or token ceiling. |
| Median and p95 latency | How long do normal and slow runs take? | Wall-clock completion time for the defined task set. |
| Retry rate | How often does the agent need another attempt? | Retries divided by runs, with automatic and human-triggered retries separated. |
| Regression rate | How often does a patch break unrelated behavior? | Runs failing regression checks divided by evaluated runs. |
| Tool calls per success | How much orchestration does a successful task require? | Total tool calls divided by successful tasks, naming the tool categories. |
| Human rework | What effort remains after the agent stops? | Minutes, edits, or review actions required to reach the rubric. |
Include the model identity, agent scaffold, reasoning setting, context window, cache policy, benchmark version, and evaluator version beside the table. Without those controls, a numerical comparison can be misleading even when the arithmetic is correct.
Use budget and frontier analyses, not one ranking
Success at a dollar or token ceiling
Choose ceilings that reflect your deployment constraint, such as a per-issue budget or a maximum token allowance. Count only tasks whose complete run—including retries—stays under the ceiling. This metric favors agents that reliably finish within a predictable budget.
Plot cost against success
Plot each agent variant with cost on one axis and verified success on the other. The Pareto frontier contains variants for which no alternative is both cheaper and more successful. A high-success variant that is dominated by a cheaper, equally successful one is not an efficient choice; a low-cost variant with materially lower success may be appropriate only when failures are cheap to recover from.
Include time and human effort
A token-efficient run can still delay a release if it spends minutes rereading context or waiting through tool calls. AWS guidance identifies reasoning latency, token throughput, tool invocation efficiency, time-to-first-token, and overall task completion time as relevant dimensions for agentic systems. Add human rework to the same decision view when engineers must repair or rewrite the result.
What published figures do—and do not—prove
Historical benchmark numbers are context, not universal rankings. OpenAI’s SWE-bench Verified announcement cited about 20% on SWE-bench and 43% on SWE-bench Lite as of August 5, 2024. Those were snapshots from that period, not current performance for every model or scaffold.
An OpenAI audit dated July 8, 2026 estimated that about 30% of SWE-bench Pro tasks were broken. That estimate is a warning to audit task validity before treating a benchmark label as proof of agent quality.
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Bai and colleagues reported up to 30× variation in total tokens across runs of the same task in 2026, and found that higher token use did not reliably improve accuracy. The same study reported about 1,000× more tokens for agentic coding than for code reasoning or code chat. Treat both figures as results of that study’s comparison, not as fixed multipliers for every workflow.
These findings explain why a single leaderboard percentage or one token average is inadequate: task validity, stochastic sampling, scaffold behavior, and accounting choices can all move the result.
A repeatable measurement procedure
- Write the rubric. Define the requested behavior, required tests, regression checks, review criteria, timeout, and what counts as invalid.
- Freeze the environment. Record repository commit, task text, allowed tools, model and agent versions, reasoning setting, context window, cache policy, and harness version.
- Instrument every request. Capture all token classes, rates, tool calls, retries, timestamps, commands, exit codes, and human interventions.
- Run without ad hoc coaching. If a person intervenes, log the intervention and classify the run separately from an autonomous run.
- Evaluate mechanically and manually. Run target and regression tests, then apply the review rubric to requirements tests cannot observe.
- Calculate per-run spend. Apply the request-time rates, including cache and reasoning charges where billed, and sum retries.
- Repeat each variant. Use the same task set and settings for multiple runs so reliability and percentile behavior are measurable.
- Publish the scorecard. Include success, cost per success, token distributions, budget success, latency, retries, regressions, tool calls, rework, and confidence intervals.
- Audit validity. Record benchmark version, task provenance, contamination controls, broken-task decisions, scaffold, model settings, and evaluator version.
- Inspect the frontier. Select a variant based on the cost, success, time, and human-effort trade-off that matches your deployment constraint.
Common measurement mistakes and fixes
Optimizing for the fewest tokens
Failure: declaring the smallest token total the winner even when it misses requirements. Fix: make verified success the denominator and publish both success and token distributions.
Counting missing telemetry as zero
Failure: underestimating spend because cache-write or reasoning fields were absent. Fix: exclude incomplete attempts from token aggregates and disclose the exclusions.
Ignoring retries
Failure: reporting only the first request while later calls produced the patch. Fix: include every request in run cost, latency, token totals, and retry rate.
Testing only the target bug
Failure: accepting a patch that fixes one assertion while breaking unrelated behavior. Fix: require both target FAIL_TO_PASS and regression PASS_TO_PASS checks, plus review where needed.
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Comparing unlike pricing models
Failure: placing a subscription fee beside API token charges without a common unit. Fix: state a transparent normalization or keep the results in separate cost categories.
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Trusting a benchmark label blindly
Failure: treating every listed task as valid and every score as comparable. Fix: preserve provenance, audit broken tasks, record contamination controls, and publish evaluator and scaffold versions.
Reporting only a mean
Failure: hiding long-tail retries and high-cost runs inside an average. Fix: publish median, p95, distributions, and confidence intervals.
A practical reporting template
For each agent variant, publish one row per task attempt and an aggregate view. The aggregate should contain:
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- Model, agent scaffold, reasoning setting, context window, cache policy, and harness version.
- Input, uncached input, cached input, cache-write, reasoning, and output token totals.
- Request-time prices and total run cost.
- Tool-call count, retry count, wall-clock completion time, and human intervention.
- Target-test result, regression-test result, review result, and final outcome category.
- Cost per successful task, success at each declared budget, token efficiency, reliability, median latency, p95 latency, regression rate, and human rework.
Keep raw traces available for audit while publishing an anonymized or summarized view when repository or customer data cannot be released. A reader should be able to tell exactly which tasks, charges, and evaluation rules produced every headline number.
How to choose an agent for a real workflow
First decide what constraint is binding. If engineering time is scarce, weight verified success, regression rate, and human rework. If API spend is capped, use success at a fixed dollar ceiling and cost per successful task. If interactive speed matters, examine p95 completion time, time-to-first-token, and tool-call delays. If workloads are bursty, inspect the tail of token and cost distributions rather than the median alone.
Then choose a point on the measured Pareto frontier and validate it on your own task mix. Keep the task rubric and telemetry schema unchanged when you re-evaluate after a model, scaffold, pricing, or benchmark update. Current provider prices, leaderboards, and task sets are volatile; refresh those inputs before making a new purchasing or deployment decision.
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