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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchNo: DeepSWE v1.1’s roughly 74% result does not mean a coding agent will resolve 74% of your production issues. It is a pass@1 leaderboard score for one model configuration on a particular benchmark snapshot—not a measured production resolution rate. The sources available do not establish that this score collapses in production, or by how much.
What the 74% DeepSWE result measures
In the official DeepSWE leaderboard snapshot dated September 22, 2026, GPT-6 Astra at xhigh reasoning effort scored 74% ±3% on DeepSWE v1.1. Epoch AI’s separate view lists the result as 74.1%. These are benchmark results for a specified model and evaluation setup; they are not a rate for coding agents generally or a forecast for any organization’s issue queue. The leaderboard can change, so the date and configuration belong with the number.
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The metric is pass@1: whether the agent’s first evaluated attempt passes the benchmark verifier. It is not the share of issues eventually resolved after multiple attempts, human intervention, retries, or production monitoring. The result reflects the evaluated model-plus-harness-and-reasoning-effort configuration and its run conditions.
DeepSWE’s official leaderboard says listed models use mini-swe-agent for consistency. The project repository describes leaderboard runs using Pier with mini-swe-agent on Modal, while Epoch AI notes that context-window failures and timeouts count as failures and provider or infrastructure errors are excluded. A percentage from a different harness, effort setting, or failure policy is not automatically comparable.
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What DeepSWE v1.1 tests
DeepSWE v1.1 contains 113 original, long-horizon software-engineering tasks across 91 active open-source repositories and five programming languages. The benchmark paper says the tasks were authored from scratch and never merged upstream, reducing exposure of reference solutions in public commit and pull-request records.
Tasks ask an autonomous agent working inside a repository to make a requested change. Hand-written program verifiers assess observable behavior rather than requiring one prescribed implementation. The repository describes isolated task environments and a separate verifier environment that applies and grades a patch in a pristine container. This is a useful test of autonomous repository work under controlled conditions, but its score describes performance on that task set and setup.
How strong is the evidence that the benchmark grades correctly?
The DeepSWE paper reports an independent LLM-judge audit of 735 DeepSWE rollouts. The judge disagreed with DeepSWE’s verifier on 10 runs, or 1.4% (reported 95% interval: 0.7–2.5%). In a comparison audit of 789 SWE-Bench Pro rollouts, it disagreed with inherited tests on 256 runs, or 32.4% (reported 95% interval: 29.2–35.8%). The disagreement counts included apparent false positives as well as false negatives.
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That audit is evidence about agreement between the judge and each benchmark’s grading method in the sampled runs. It is not proof that every verdict is correct, nor evidence about how often an agent succeeds on production work. The two percentages also describe different benchmark audits, not competing production resolution rates.
Why a benchmark score may not transfer to production
The paper scopes DeepSWE to autonomous repository work and identifies several limits relevant to interpreting its score. These explain why a direct conversion to a company’s expected success rate is not justified; they do not demonstrate a particular production collapse.
- Different task mix: DeepSWE emphasizes long-horizon repository tasks. The paper says shorter work, including small single-file edits and bug localization, is under-represented. A team’s issue mix may look quite different.
- Different tools and constraints: The benchmark uses a fixed harness rather than the vendor-tuned products developers use day to day. Production repositories, permissions, available tools, network access, test quality, and operational rules may also differ.
- Different success definition: A benchmark verifier checks specified observable behavior in a controlled environment. A production team may additionally require review approval, compatibility, security, maintainability, deployment success, or acceptance by the person who filed the issue. Those criteria must be defined before comparing rates.
- Different denominator: Pass@1 counts one evaluated attempt per task. A production rate could count issues opened, issues assigned to an agent, attempts completed, or changes merged. Those denominators answer different questions.
- Leaderboard separation is not external validation: The paper cautions that a wider score spread can help distinguish systems, but does not itself establish capability or correlate with external software quality; that correlation was not measured.
The paper also reports that DeepSWE prompts are about half the length of SWE-Bench Pro prompts, while reference solutions touch 5.5 times more code; its abstract reports about twice as many output tokens. These are benchmark-to-benchmark comparisons, not measurements of ordinary production tickets or evidence that one benchmark predicts real-world performance better.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What would establish a real production gap?
To claim that a benchmark score falls to a particular production resolution rate, an evaluation would need deployment data from a defined population of tasks. At minimum, it should report which tasks were eligible, how many were attempted, what counted as resolution, whether retries and human edits counted, and over what period results were measured. Without those details, “collapse” has no stable numerical meaning.
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- Task provenance: whether tasks are original, public, or drawn from the organization’s own work, and what contamination exposure is possible.
- Task scope: task duration and complexity, repository and language mix, and representation of short edits, debugging, and larger changes.
- Grading: what the verifier checks, whether it reflects the team’s acceptance criteria, and whether outcomes receive an independent audit.
- Agent setup: model, reasoning effort, harness, tools, sandbox, network access, and permissions.
- Metric and uncertainty: attempts per task, treatment of timeouts and infrastructure errors, uncertainty intervals, and run-to-run variation.
- External validity: how closely the evaluated tasks and success criteria match the production workload where the result will be used.
Raw percentages should not be compared until the task populations, configurations, metrics, and failure rules align. For an internal pilot, report both first-attempt success and eventual resolution separately, and preserve the task-level denominator so that retries or human assistance do not silently change what “resolved” means.
How to read the headline claim
The supported statement is narrow: GPT-6 Astra at xhigh reasoning effort scored approximately 74% ±3% pass@1 on the September 22, 2026 DeepSWE v1.1 leaderboard snapshot. The unsupported extension is that coding agents resolve 74% of production issues—or that this rate has been shown to collapse after deployment. The benchmark offers a structured signal about a particular evaluation; a production rate requires separately defined and measured deployment outcomes.
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