Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
The latest results on Specific Labs’ Real-SWE benchmark do not support saying the best coding agent gets roughly six in ten changes wrong: its September 2026 snapshot lists GPT-6 Astra with Codex CLI at 46.25% resolution, meaning 53.75% of attempts were unresolved by the benchmark’s criterion. A 38.8% result behind the “six in ten” claim was reported by a secondary article, but it is not the benchmark owner’s current listed leader.
Where the “six in ten” claim comes from
The figure traces to a 38.8% resolution result reported by Jarvis AI in 2026. That implies 61.2% unresolved attempts in that reported result, but the benchmark owner’s September 2026 page snapshot shows a higher leading result. The difference matters: the claim describes a particular reported benchmark result, not a stable rate for coding agents as a category.
Specific Labs’ Real-SWE page, accessed October 8, 2026, lists the following model-and-harness combinations:
| Model and agent harness | Resolution rate | Unresolved share |
|---|---|---|
| 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% |
These are Real-SWE results from the same September 2026 page snapshot, not estimates of how often each setup will succeed on your repository. “Unresolved” is the complement of the benchmark’s resolution rate; it does not establish that every such attempt produced incorrect code or a change that would be rejected in ordinary development.
#1 Best Overall
- Careercup, Easy To Read
- Condition : Good
- Compact for travelling
What Real-SWE measures—and what it does not
Specific Labs says Real-SWE evaluates agents on private production codebases licensed from real companies. It tests model-and-harness combinations using vendors’ native agent harnesses, and says the models used high reasoning. Its page defines the metric this way: “Resolution rate is equivalent to pass@1, averaged over eight independent runs per task.” See the Real-SWE benchmark page.
The displayed snapshot covers 10 sample tasks, with eight rollouts per model per task. Results therefore reflect performance on that task set and evaluation procedure; they are not a direct estimate for every engineering team, repository, or type of change. Because each result pairs a model with a harness, the table also cannot isolate model capability from the tools and workflow supplied by the harness.
Most importantly for the headline’s “review load” claim, the benchmark reports task resolution—not human review time, the proportion of submitted pull requests needing correction, or the engineering cost of checking an attempt. A 53.75% unresolved share is not a measurement that reviewers must fix 53.75% of production changes.
Why the task mix changes the picture
Aggregate scores can conceal sharp differences between tasks. In the same page snapshot, the Analytics stream reducer task had 0.0% aggregate resolution across the listed model runs. The benchmark’s examples also illustrate failure modes including unverified assumptions, missed requirements, integration errors, regressions, and changes to the wrong files.
That spread is why a single overall percentage should not be treated as a forecast for a particular issue. A small, well-specified change in a familiar part of a codebase may pose a different challenge from a task involving hidden assumptions or interactions across components. Real-SWE’s 10 disclosed tasks are useful evidence about the benchmark, but too limited to establish a universal success or failure rate across coding work.
What this means for reviewing AI-generated changes
Human review remains a sensible operational safeguard, but the benchmark does not quantify how much review a team will need. Review the change against the actual request and repository behavior rather than treating an agent’s completion message or a benchmark percentage as proof.
- Check requirements: Compare the diff and tests with each requested behavior, including constraints that are easy to overlook.
- Verify assumptions: Confirm that the agent’s interpretation matches repository code, configuration, and documented behavior.
- Inspect integration: Look at how edits fit surrounding components and established interfaces, not just whether the changed file appears plausible.
- Look for regressions: Run relevant tests and examine affected paths for unintended behavior changes.
- Confirm the scope: Make sure edits are in the files that should change and that the running application reflects the intended result.
These are practical review checks suggested by the types of benchmark failures; they are not a measured checklist proven to reduce Real-SWE’s unresolved rate.
Recommended Free Tools
How to use the numbers responsibly
When comparing agent results, keep the model and harness paired, use the same benchmark snapshot and task set, and look beyond the aggregate resolution rate to task-level outcomes. Specific Labs’ page also estimates per-rollout costs from $2.50 to $6.96 across listed setups; those are benchmark estimates, not stable current market quotes. A cost comparison is meaningful only alongside the evaluation context and the work the result represents.
Best Value
For a team deciding whether to use an agent, the benchmark is one signal—not a substitute for evaluating it on representative work under the team’s own review and testing practices. The strongest conclusion supported by this snapshot is narrower: even its listed leader did not resolve every attempt on this benchmark, while the benchmark does not say how much human correction those attempts required.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
