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Before ranking coding agents, separate failures caused by the evaluation infrastructure from failures that reflect an agent’s attempt. Then publish both counts, the resource policy, and the scoring rule. Otherwise, a score can make an unreliable runtime look like a weak agent—or make a more generous compute budget look like a capability improvement.
Why infrastructure failures belong outside the agent score
A coding-agent benchmark evaluates a system acting inside a runtime environment. The environment’s resource limits and enforcement can determine whether a run proceeds, and can also shape which strategies the agent can use. A score without its execution context is therefore incomplete.
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Anthropic’s February 2026 Terminal-Bench 2.0 experiment held the model, harness, and task set constant while changing resource configurations. Total success rose by 6 percentage points from the strictest configuration to uncapped resources. Infrastructure errors fell from 5.8% under strict enforcement to 0.5% when uncapped, and to 2.1% with three-times headroom. These figures are specific to that experiment, not a general failure rate for coding-agent evaluations. Anthropic’s experiment and analysis also found that extra capacity beyond roughly three times task resource specifications could enable resource-intensive approaches and improve task success, not merely prevent runtime errors.
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Classify each run before scoring it
Use separate labels for an execution failure and an unsuccessful attempt. The point is not to excuse a poor result; it is to attribute the result to the right part of the evaluated system.
Infrastructure failure
Use this when the run cannot meaningfully measure agent capability because the execution system failed—for example, a pod or container error, or a resource-driven termination before the agent could carry out the task. Anthropic documented both pod failures unrelated to the model’s problem-solving and container terminations caused by resource limits.
Agent/task failure
Use this when the run executed sufficiently to assess the agent, but it did not produce the required outcome. A failed verifier result after a meaningful attempt belongs here, not in the infrastructure category.
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Use this description when a configuration changes what the agent can attempt, rather than simply preventing an accidental runtime failure. A large memory allowance, for example, might make a resource-intensive test suite or dependency operation feasible. This is not automatically a faulty run; it is a different evaluation condition that should be disclosed.
What to record for every run
Keep a run-level record so readers can see what was compared and how failures were handled. A useful reporting schema includes:
- Agent and model version.
- Benchmark, task, and task-set version.
- Harness, tool, and verifier versions.
- CPU and memory allocation, resource guarantees, hard limits, and enforcement policy.
- Timeout and whether temporary resource spikes are tolerated.
- Exit status, verifier outcome, and error category.
- Whether the agent made a meaningful attempt.
- Any rerun or exclusion decision, including which result enters the primary score.
Retain the original row when a run is rerun. Publish raw totals and the exact rule used to compute any adjusted score. This schema is a practical reporting recommendation; it is not a claim that every benchmark currently uses the same fields.
How to compare rankings fairly
Before naming a winner, check whether the agents faced the same evaluation conditions and whether the reported metric answers the question you care about. A useful comparison covers these axes:
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- Outcome: task pass rate or verifier result, with infrastructure failures shown separately.
- Reliability: repeat attempts, consistency, partial completion, and failure categories.
- Resource and time budget: CPU and RAM, hard caps versus guaranteed floors, timeout, and tolerance for temporary spikes.
- Execution stack: benchmark and task versions, task mix, harness, toolchain, and verifier.
- Uncertainty: sample size, confidence intervals, repeated attempts, and the rule for ties.
- Efficiency: cost, token use, and wall-clock time, where reported. Keep these separate from correctness.
Small score gaps deserve particular caution. Anthropic recommends skepticism about differences below 3 percentage points until configurations are documented and matched. That is guidance from one provider’s study, not a universal statistical cutoff; sample size and uncertainty still matter.
Read composite scores alongside their components
A composite rank compresses multiple measurements into one number; it is not a guarantee for a particular repository or workload. Look at the task mix, component scores, and scoring method before applying an overall rank to your own use case.
| Evaluation | Published scope and method | What to keep in mind |
|---|---|---|
| Artificial Analysis Coding Agent Index v1.5 | Its methodology, current from September 2026, describes an equal-weight composite across DeepSWE v1.1 (113 tasks), Terminal-Bench 4.0 (66 tasks), and SWE-Atlas-QnA (124 tasks): 303 tasks total, with three attempts per task. It reports component results as well as the aggregate and describes separate efficiency measurements. | Use the component results to understand what the aggregate represents; do not treat the index as a universal workload guarantee. |
| Sigmabench methodology v1 | Frozen in December 2025; separates accuracy, partial-patch consistency, and time utilization. It uses 5,000 bootstrap samples for confidence intervals and assigns equal ranks when its comparison rule cannot distinguish agents. | Its documented scope has limits: generic toolchains, open-source-only tasks, no interactive evaluation, and CLI-only agents. |
| JetBrains Kotlin Benchmark, first public iteration | Its July 2026 announcement describes 105 tasks from active open-source repositories, verified in containerized environments. The top reported result was 90 of 105 tasks (85.71%). | JetBrains said this first iteration did not yet include the most recent model releases, and cautioned that scores are “intended as a signal, not a guarantee for every codebase.” |
These figures come from different benchmarks and methods; they are not comparable failure rates or a single cross-benchmark ranking. See the Artificial Analysis index methodology, Sigmabench methodology, and JetBrains Kotlin Benchmark announcement for their stated designs and scope.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What resource headroom changes in practice
In Anthropic’s experiment, strict Kubernetes enforcement guaranteed per-task resources but also killed containers that exceeded the limit. The Terminal-Bench leaderboard used a different sandboxing provider that allowed temporary overallocation. That mismatch helped produce both infrastructure errors and a score discrepancy.
Anthropic observed that headroom up to around three times the task resource specifications mainly improved reliability by absorbing transient spikes. Above that level, additional capacity could support approaches such as pulling large dependencies, spawning expensive subprocesses, or running memory-intensive test suites. Those approaches can raise success for reasons beyond fewer infrastructure failures. A sound report therefore names the resource policy and avoids treating every resource-related result as either “just infrastructure” or purely agent capability.
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What a ranking can and cannot establish
A ranking can summarize performance under a specified benchmark, task mix, runtime, budget, and scoring method. It cannot, by itself, establish how an agent will perform on every language, repository, or workflow. A 2026 technical review describes agent reliability as a system property involving the harness, execution state, retrieval, memory and state management, permissions, review interfaces, and resource allocation; it also notes that evidence strength varies and outcomes depend on workload and configuration. The review’s stated scope and limitations are relevant when interpreting claims that extend beyond one benchmark.
There is no established universal percentage for how often infrastructure failures distort coding-agent rankings across providers. The evidence supports labeling failures, matching configurations, and disclosing scoring decisions—not an industry-wide prevalence estimate.
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