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No. DeepSWE v1.1’s roughly 74% result is a pass@1 score for one model configuration on a defined benchmark—not a measured rate of resolving production issues. The available sources do not establish that the score collapses in deployment; they show why it should not be treated as a production forecast.
What the 74% DeepSWE score actually measures
The official DeepSWE leaderboard’s September 22, 2026 snapshot reports GPT-6 Astra at 74% ± 3% for the xhigh reasoning-effort configuration. Epoch AI’s separate benchmark view lists 74.1%. Both figures refer to DeepSWE v1.1 leaderboard performance, not to a survey of deployed coding agents or a count of issues resolved in customers’ repositories.
The metric is pass@1: whether an agent’s first evaluated attempt passes the benchmark verifier. It is not the percentage of all production tickets the model will solve, nor a guarantee that one attempt will succeed on a particular team’s work. The score belongs to the evaluated model, harness, reasoning-effort setting, and run conditions together.
Leaderboard entries use mini-swe-agent at specified reasoning-effort levels for consistency. Epoch AI says context-window failures and timeouts count as failures, while provider and infrastructure errors are excluded. The DeepSWE repository also describes leaderboard runs using Pier with mini-swe-agent on Modal. These details matter: changing the harness or how failures are counted can change what a reported score represents.
#1 Best Overall
What DeepSWE v1.1 tests
DeepSWE v1.1 contains 113 original, long-horizon software-engineering tasks spanning 91 active open-source repositories and five programming languages. The DeepSWE paper says the tasks were authored from scratch and were not merged upstream, reducing the chance that reference solutions are exposed in public commit or pull-request history.
Tasks ask an autonomous agent to make a requested repository change whose observable behavior is judged by a hand-written functional verifier. The official repository describes isolated task environments and a separate verifier environment that applies and grades the patch in a pristine container. A passing result therefore means the submitted change satisfied that task’s verifier under the benchmark’s setup; it does not establish that every desired property of a production change was tested.
What the verifier audit supports—and what it does not
The paper reports an independent LLM-judge review of sampled benchmark runs. Its figures measure agreement between that judge and the benchmark’s existing grading methods, not the share of tasks that would succeed after deployment.
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|---|---|---|
| DeepSWE verifier, 735 reviewed rollouts | 10 disagreements (1.4%; reported 95% interval: 0.7–2.5%) | In this sample, the independent judge and DeepSWE verifier usually agreed. |
| SWE-Bench Pro inherited tests, 789 reviewed rollouts | 256 disagreements (32.4%; reported 95% interval: 29.2–35.8%) | In this sample, the judge and inherited tests disagreed more often. |
The disagreement counts include apparent false positives and false negatives. The comparison is evidence about agreement in the sampled audit, not universal proof that one grading method is valid for every task, and it says nothing directly about production resolution rates.
Rank #3
Why benchmark performance may not transfer to a production workflow
The task mix may be different
The paper explicitly focuses on autonomous repository work and says short tasks, such as small single-file edits and bug localization, are under-represented. A production queue may contain a different mix: routine fixes, unclear reports, dependency or deployment problems, requests that need clarification, and work spread across repositories or services. A benchmark score cannot predict performance on that queue unless the task distribution is sufficiently similar.
The evaluated setup is not a day-to-day product workflow
DeepSWE uses a fixed harness rather than the vendor-tuned products developers may use in daily work. Production conditions can also differ in repository access, permissions, network and sandbox rules, available tools, test quality, review requirements, and operational constraints. Those differences are reasons to measure a system in its intended workflow, not evidence that a particular model’s score necessarily rises or falls there.
A benchmark comparison is not an external quality study
The paper notes that a wider score spread can help distinguish systems on the benchmark, but spread alone is not a capability claim: the authors did not measure correlation with external software quality. The paper also reports benchmark-to-benchmark differences: DeepSWE prompts are about half the length of SWE-Bench Pro prompts, reference solutions touch 5.5 times more code, and its abstract reports about twice as many output tokens. These figures characterize those benchmark comparisons; they are not measurements of ordinary production tasks.
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Is there evidence that the 74% rate “collapses” in production?
The sources available for this result do not document a production deployment showing a collapse, or provide a production success rate to compare with the leaderboard. “Collapse” is therefore an unverified claim, not a conclusion supported by the benchmark or the verifier audit. Establishing it would require deployment data with a defined population, task denominator, success criteria, and evaluation period.
Best Value
In particular, a production report would need to make clear whether its denominator includes all assigned issues, only issues accepted by the agent, or only attempts that reached verification. It would also need to distinguish first-attempt success from success after retries, human intervention, or edits by a reviewer. Without those definitions, a production percentage is not directly comparable with pass@1.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use DeepSWE when choosing or evaluating a coding agent
Treat the leaderboard as evidence about performance on its own tasks and setup. For a deployment decision, build an evaluation around the work the agent will actually receive and the conditions under which it must operate.
Quick Recap
- Define the target outcome. Decide what counts as a resolved issue, including whether tests must pass, reviewers must accept the change, or the fix must remain successful after deployment.
- Use a representative task set. Sample real work across the relevant repositories, languages, task sizes, and levels of ambiguity. Keep separate categories visible rather than allowing one easy category to dominate an overall rate.
- Fix the attempt and assistance rules. Record the model, harness, reasoning effort, tools, permissions, and whether retries or human help are allowed. Report first-attempt results separately from outcomes after intervention.
- Apply consistent verification. Use tests and review criteria suited to the work, and document how failed, timed-out, incomplete, or unscorable attempts are counted.
- Compare like with like. Before comparing two benchmark percentages, align task provenance and contamination exposure, task horizon and repository/language mix, verifier design, harness and sandbox conditions, metric and attempt count, uncertainty, and relevance to the production task distribution.
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