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To find out what a coding-agent workflow costs in real repository work, divide the total cost of every attempt—including failed attempts and retries—by the number of patches that pass acceptance checks you defined in advance. Token prices show one input cost; cost per passing patch connects the bill to accepted work.
What does cost per passing patch measure?
Cost per passing patch is an editorial calculation, not a formally standardized industry metric:
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Cost per passing patch = total attributable cost of the evaluation run ÷ number of patches that pass the predeclared acceptance rule
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Decide how to treat review and infrastructure costs before comparing systems. You can include them in the numerator, or report them separately, but do not silently include one category for one workflow and exclude it for another. If the run produces no passing patches, the ratio is undefined; report zero passes and the total cost rather than presenting the result as a zero-cost patch.
Define “passing” before you run the test
A passing patch means only that a specific output met a specific acceptance rule on a particular task set and test setup. State the dataset and version, the repository tasks, and the exact checks used. A benchmark’s resolution rate is not interchangeable with an internal team’s production acceptance rate.
For a fair comparison, decide whether each task contributes at most one accepted patch—for example, the final patch under a fixed retry and stopping policy. Count a task only once even if several attempts pass. This keeps the denominator aligned with accepted task outcomes rather than rewarding workflows simply for generating more candidate patches.
What benchmark tests can establish
SWE-bench uses real GitHub issues: an agent receives a repository and an issue description, then attempts to produce a resolving patch. OpenAI’s explainer, “Introducing SWE-bench Verified,” updated February 24, 2025, describes two test groups: FAIL_TO_PASS tests for the reported issue behavior and PASS_TO_PASS tests intended to catch regressions. OpenAI states: “Both sets of tests are required to pass for the edit to fully resolve the original GitHub issue.”
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A green result is evidence under those tests, not proof of universal correctness. OpenAI’s account of SWE-bench Verified identifies underspecified issues, overly specific or unrelated tests, and environment setup failures as sources of misleading outcomes. Automated results are useful only when interpreted in light of the task and test quality.
How to run a cost comparison teams can trust
- Choose the task set. Use the same repositories, task versions, and acceptance tests for every workflow. Record the exact dataset or internal task-set version so a later run can be reproduced.
- Freeze the workflow. Keep the tools, execution environment, retry budget, stopping rule, and test procedure consistent. If those differ, the result compares different workflows, not just different model or routing choices.
- Write the acceptance rule. Specify which tests must pass, how patch application is handled, and how you count tasks that have multiple attempts. Set these rules before viewing results.
- Log the full cost ledger. Capture billed model and agent/runtime costs, tool use, and retries for successful and unsuccessful attempts. Record review and infrastructure costs separately if they are not included in the headline numerator.
- Classify outcomes. Separate passing patches, genuine patch failures, and evaluation infrastructure errors. Do not count an infrastructure error as a passing patch or hide it inside a failure category. Decide in advance whether the workflow permits a rerun after an infrastructure error, and log its cost.
- Calculate and report the result. Divide total attributable cost by passing patches. Show the numerator and denominator next to the ratio, along with the pass count and share, retry and tool-call use, and latency if it matters to your workflow.
- Keep the run artifacts. Preserve per-task reports, test output, logs, and applied diffs so failures and results can be checked. The SWE-bench evaluation guide distinguishes resolved and unresolved tasks, empty patches, and instances with errors, and describes these kinds of output artifacts. It also notes that results are cached by run ID and instance ID; use a new run ID when changing predictions for an instance already evaluated.
Read the ratio alongside its numerator and denominator
The ratio compresses the economics of accepted work into one number, but small samples can make it unstable. A workflow with one passing patch out of a few tasks can look very different after one additional success or failure. Report the number of tasks, number of passing patches, pass share, total cost, and cost per passing patch together. Include retry and tool-call counts so readers can see whether the apparent savings came with substantially more work or a different failure pattern.
Consider patch quality beyond automated tests when a review process is available. A patch that passes tests but needs substantial correction may not be equivalent to one accepted with little or no rework. If you measure reviewer time or post-merge fixes, state the method and report them separately or include them consistently in the cost ledger.
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Latency is another separate axis: a cheaper passing patch may still be unsuitable when turnaround time is critical. Report it when relevant, but do not let a fast completion obscure a low pass rate or a high cost per accepted result.
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What one reported routing example shows—and does not
Tyler Folkman reported four passing patches for $2.74 using Sol alone, compared with three for $11.12 using a Sol-to-Kimi handoff. On those reported figures, the implied costs are about $0.69 and $3.71 per passing patch, respectively.
| Reported workflow | Passing patches | Reported cost | Implied cost per pass |
|---|---|---|---|
| Sol alone | 4 | $2.74 | About $0.69 |
| Sol-to-Kimi handoff | 3 | $11.12 | About $3.71 |
These are calculations from one personal report, not an independently verified benchmark or a general estimate of either model or routing pattern. The example illustrates why comparing token prices alone can miss the cost of retries, routing, and unsuccessful work; it does not establish which workflow is better for another team.
Why no universal winner follows from token prices
Token rates alone omit whether an attempt passes, how many retries it takes, how much tool or agent work is billed, and whether the result meets the team’s own acceptance bar. No current same-harness, same-task comparison with verified current prices is established here, so a universal model or routing winner cannot be named. Teams should measure their own task set under a fixed workflow and publish the cost ledger and passing count alongside the ratio.
Benchmark quality matters to the denominator
OpenAI described SWE-bench Verified as a 500-sample subset. During construction, 93 software developers with Python experience screened samples; an analysis set of 1,699 randomly selected samples was annotated. OpenAI reported that 38.3% were flagged for underspecified problem statements, 61.1% for tests that might unfairly reject valid solutions, and 68.3% were filtered for one or more quality concerns. These percentages refer to the annotated analysis set, not the final 500-sample subset. They underline why benchmark task and test quality affect what a “pass” means.
Historical model scores in that explainer reflect earlier evaluation context, not current rankings. Use them neither as a current price comparison nor as a substitute for a controlled evaluation on your own work.
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