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Before ranking coding agents, separate infrastructure failures from runs in which the agent had a fair chance to complete the task. Record the runtime and resource policy, retain failed-run counts, and compare agents only on matched benchmark configurations. Otherwise, a score can reflect container limits, timeouts, or harness behavior as much as coding ability.
What counts as an infrastructure failure?
A coding-agent benchmark measures a system acting inside a runtime environment, not a model in isolation. A run should be labeled an infrastructure failure when execution breaks in a way that prevents a meaningful assessment of the agent—for example, a pod or container failure, or a resource-enforced termination before the agent can carry out the task.
An agent/task failure is different: the run proceeds far enough to assess the agent, but the required result is not achieved. Keep setup faults, runtime faults, and unsuccessful task outcomes in distinct categories rather than treating every non-pass as the same result.
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Why can resource limits change benchmark scores?
Anthropic’s February 5, 2026, Terminal-Bench 2.0 experiment ran the same Claude model, harness, and task set under six resource configurations. The strictest setup guaranteed per-task resources but killed containers when they exceeded the limit. The benchmark leaderboard used a different sandboxing provider that allowed temporary overallocation, producing both infrastructure errors and a score discrepancy. Anthropic’s experiment and analysis show why a headline score needs its execution context.
In that experiment, the total success rate was 6 percentage points higher with uncapped resources than under the strictest configuration. Infrastructure errors fell from 5.8% under strict enforcement to 0.5% when uncapped, and to 2.1% with three-times headroom. These are results from that specific Terminal-Bench 2.0 setup, not a universal infrastructure-failure rate.
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The effect was not only about preventing failed containers. Anthropic observed that headroom up to roughly three times task resource specifications mainly improved reliability during transient resource spikes. Above that level, extra capacity also enabled resource-intensive approaches, such as pulling large dependencies, spawning expensive subprocesses, and running memory-heavy test suites. More resources can therefore repair execution reliability and give agents a different computational advantage; the two effects should not be conflated.
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What should a fair coding-agent comparison control?
Compare the evaluation setup as carefully as the score. Anthropic cautions that “Two agents with different resource budgets and time limits aren’t taking the same test.” Before naming a winner, align or disclose these factors:
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- Benchmark and task set: benchmark version, task versions, task mix, and verifier.
- Resources and time: CPU and memory allocation, hard caps versus guaranteed floors, timeout, and whether temporary resource spikes are tolerated.
- Execution stack: harness, toolchain, sandboxing or container environment, and relevant versions.
- Attempt policy: number of attempts per task, how repeats are handled, and whether results reflect the first attempt or another rule.
- Failure accounting: which outcomes count as infrastructure failures, task failures, or exclusions, and how each affects the reported score.
- Uncertainty and efficiency: sample size, confidence intervals or tie policy, plus cost, token use, and wall-clock time where reported. Keep efficiency separate from correctness.
When configurations differ, a score gap cannot by itself show that one agent is more capable. Anthropic recommends skepticism about gaps below 3 percentage points until configurations are documented and matched. That is a recommendation from one provider’s experiment, not a universal statistical cutoff.
How should failed and rerun tasks be reported?
Keep a per-run record that lets readers distinguish what the agent did from what the environment allowed. A practical record includes:
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- Agent and model version; harness and tool versions.
- Task and benchmark version.
- CPU and memory allocation, enforcement policy, and timeout.
- Exit status and verifier outcome.
- Error category and whether the agent made a meaningful attempt.
- Any rerun or exclusion decision, including which result enters the primary score.
Publish raw totals alongside any adjusted score, and state the exact rule used to calculate that score. If a run is rerun, preserve and disclose the original result rather than silently replacing it. A reader should be able to tell how many tasks passed, how many failed after meaningful attempts, how many were blocked by infrastructure, and how reruns changed the published figure.
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Composite rankings can help summarize performance, but their components and methods determine what the rank means. The Coding Agent Index v1.5 methodology from Artificial Analysis, identified as current from September 2026, gives equal weight to three components and reports results alongside its aggregate:
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| Index component | Tasks | Attempts per task |
|---|---|---|
| DeepSWE v1.1 | 113 | Three |
| Terminal-Bench 4.0 | 66 | Three |
| SWE-Atlas-QnA | 124 | Three |
| Total | 303 across 3 benchmarks | Three per task |
The methodology also describes task-level attempts and separate efficiency measurements. Consult the Artificial Analysis Coding Agent Index methodology when interpreting its index or comparing its component scores.
Sigmabench separates accuracy, partial-patch consistency, and time utilization. Its methodology uses 5,000 bootstrap samples for uncertainty estimates and assigns equal ranks when its comparison rule cannot distinguish agents. The method is v1, frozen in December 2025, and has stated limits: generic toolchains, open-source-only tasks, no interactive evaluation, and CLI-only agents. Its benchmark methodology helps explain what its results include and exclude.
Neither a composite index nor a tie rule guarantees how an agent will perform on a particular repository or workload. For a language-specific example, JetBrains’ July 2026 Kotlin Benchmark used 105 tasks from active open-source repositories, verified in containerized environments. Its first reported run’s top result was 90 of 105 tasks (85.71%); JetBrains said that iteration did not yet include the most recent model releases. It describes the scores as “a signal, not a guarantee for every codebase.” See the JetBrains Kotlin Benchmark announcement for the scope and qualification.
How can you interpret a close score or choose an agent?
Use benchmark results as evidence about a defined workload and configuration, not as a universal ordering. Inspect component results, infrastructure-failure counts, uncertainty, and the budget used before treating a narrow gap as meaningful. If your own workload matters, give agents matched tasks, tools, resource limits, and timeouts, and report correctness separately from speed or cost.
Reliability is also a system property: harness behavior, execution state, retrieval, memory and state management, permissions, review interfaces, and resource allocation can all shape the outcome. A technical review by Stephanie Jarmak, version 1.0.0 from August 2026, discusses this broader frame while noting that evidence strength varies and results depend on workload and configuration. The review of coding-agent reliability is useful context, but does not establish a single cross-provider failure rate.
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