Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe “about six in 10” claim needs a date and a benchmark attached. In Specific Labs’ Real-SWE page snapshot from September 2026, the listed leader—GPT-6 Astra paired with Codex CLI—resolved 46.25% of benchmark attempts. That means 53.75% did not meet the benchmark’s resolution criterion, not that reviewers were proven to spend a particular amount of time fixing that share of real-world code changes.
What does “gets changes wrong” mean here?
Specific Labs describes Real-SWE as a benchmark using private production codebases licensed from real companies. It evaluates model-and-harness combinations using vendors’ native agent harnesses, with all models run at high reasoning. The benchmark page states: “Resolution rate is equivalent to pass@1, averaged over eight independent runs per task.” In practical terms, the score is the share of attempts that satisfy the benchmark’s resolution criterion across those runs; an unresolved attempt is not automatically proof that a change would be wrong in every engineering context.
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The currently displayed September 2026 snapshot lists 10 disclosed tasks and eight rollouts per model per task. These results describe performance on that task set, not a representative sample of all engineering work, repositories, or types of change. The benchmark also evaluates a model together with its harness, so it cannot isolate model capability from the tools and workflow surrounding it.
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These figures are from Specific Labs’ Real-SWE page snapshot accessed October 8, 2026. The percentage not resolved is calculated as 100% minus the listed resolution rate.
#1 Best Overall
| Model and harness | Resolution | Not resolved |
|---|---|---|
| GPT-6 Astra with Codex CLI | 46.25% | 53.75% |
| Fable 5.1 with Claude Code | 45.00% | 55.00% |
| Gemini 3.8 Flash with Gemini CLI | 38.75% | 61.25% |
The percentages not resolved are arithmetic complements of the benchmark’s results, not a separate measurement of incorrect code, rejected pull requests, or human correction work. The benchmark page also estimates rollout costs from $2.50 to $6.96 across its listed setups; those are benchmark estimates, not stable market prices.
Why the “six in 10” wording is snapshot-dependent
A 2026 Jarvis AI article reports a 38.8% top result, which corresponds to roughly 61.2% not resolved. That is the source of the approximate six-in-ten framing. It differs from Specific Labs’ currently displayed September 2026 table, whose top result is 46.25%, or 53.75% not resolved. The two claims should not be blended: one is a secondary article’s reported figure, while the other is the benchmark owner’s current table for a particular model-and-harness pairing.
When quoting a coding-agent benchmark, name the source snapshot, task set, model, and harness. A percentage without those details can make a dated result sound like a timeless rate for every coding agent.
What benchmark failures can tell a reviewer to check
Specific Labs’ task outcomes vary substantially. In the September 2026 snapshot, the analytics stream reducer shows 0.0% aggregate resolution across the listed model runs. The benchmark’s failure categories include unverified assumptions, missed requirements, integration errors, regressions, and changes to the wrong files. These are useful review prompts, but the overall resolution score does not reveal how often each failure occurs.
- Requirements: Compare the change with the task’s stated behavior, including edge cases and constraints.
- Repository assumptions: Confirm that claims about existing code, data, and interfaces match the repository rather than an agent’s inference.
- Integration: Check that the change fits surrounding components and does not leave necessary wiring or dependent updates out.
- Regressions: Inspect affected behavior beyond the new path and run relevant tests.
- Scope: Verify that edits are in the intended files and that the running application actually uses the modified code.
These checks are operational safeguards suggested by the kinds of failures the benchmark identifies; Real-SWE does not quantify how much review time they take or how many production pull requests need correction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the result does—and does not—say about review load
It is reasonable to treat coding-agent output as work that needs human verification, particularly when the code affects important behavior. But “review load” is an implication, not a measured Real-SWE outcome. The published results report task resolution; they do not report reviewer hours, production pull-request correction rates, or the share of submitted changes that humans must repair.
Nor should the benchmark’s unresolved share be treated as a forecast for a particular team. The task set has 10 disclosed examples, and performance depends on the paired model and harness as well as the benchmark’s tasks and resolution criterion. For an organization, the useful question is how its own repository, task types, tests, and review process perform—not whether one benchmark percentage predicts every change.
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Sources: Specific Labs’ Real-SWE benchmark; Jarvis AI’s 2026 article.
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