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The fairest way to compare AI coding agents is to evaluate the complete agent system on a reproducible portfolio of real repository, terminal and code-understanding tasks. Pin the model, agent harness, tools, repository revision, container, time limit, retry budget and sampling settings. Grade behavior with deterministic tests, then inspect transcripts for invalid tool use, reward hacking and process failures. Report pass rate alongside cost, tokens, wall-clock time, timeout and error rates, and results for each task family.
A single leaderboard number can hide large differences in terminal competence, repository comprehension, reliability and operating cost. A useful evaluation therefore treats an agent-model-harness configuration as the unit being compared, audits task quality and contamination, and publishes enough detail for another team to reproduce the run.
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What exactly are you comparing when you test an AI coding agent?
You are comparing a system, not just a language model. The measured result depends on the model version, agent loop, prompt, tools, context management, shell permissions, network policy, repository state, test command, time limit and retry behavior. Calling the result a score for a model alone gives readers more certainty than the experiment supports.
Define the unit under test as an agent-model-harness combination. For example, a named coding agent paired with a pinned model release, a specified reasoning setting and a documented tool wrapper is one configuration. Changing the harness, tool permissions or retry policy creates a different configuration even if the model name stays the same.
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Why the harness can change the result
- One harness may let the agent search files, run tests and edit repeatedly; another may restrict shell access or require a patch-only response.
- Automatic retries can convert an initial failure into a pass, but they also increase cost and wall time. Report the retry budget rather than silently folding retries into the score.
- Network access can expose documentation or package data unavailable to another agent. Record whether the run had no network, an allowlist or unrestricted access.
- Different context windows, truncation rules and tool-output limits alter how well an agent can understand a large repository.
Which task families should a realistic comparison include?
Use at least three families and show their scores separately: repository repair or feature work, terminal-heavy end-to-end work, and repository question-answering or code tracing. They exercise different abilities, so a composite can conceal a serious weakness.
Repository repair and feature work
Give the agent a real issue or a tightly specified feature in a pinned repository revision. Success should require a working change, regression protection and preservation of existing behavior. Record the files changed and the tests that pass, not merely whether the agent produced a patch.
Terminal-heavy end-to-end tasks
These tasks test whether an agent can navigate a shell, inspect environment state, install or use dependencies, run a sequence of commands and recover from failures. They reveal practical tool competence that a patch-only benchmark cannot measure. Keep the container image and available commands identical across agents.
Repository Q&A and code tracing
Ask questions that require locating definitions, following control flow, identifying configuration effects or explaining a data path. Grade answers against repository evidence. This family separates code comprehension from the ability to modify files successfully.
Artificial Analysis uses this three-part shape in its current coding-agent index: FrontierCode v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA-AA are equally weighted. Its methodology lists 100 FrontierCode v1.1 Main software-engineering tasks, 66 Terminal-Bench 4.0 tasks and 124 SWE-Atlas-QnA-AA tasks. See the Artificial Analysis coding-agent methodology for the versioned definitions.
| Task family | What it measures | Published example and size |
|---|---|---|
| Repository repair or feature work | Implementing behavior in an existing codebase, preserving compatibility and passing tests | FrontierCode v1.1 Main: 100 tasks |
| Terminal-heavy execution | Shell navigation, environment handling, command sequencing and recovery | Terminal-Bench 4.0: 66 tasks |
| Repository Q&A or code tracing | Finding definitions, tracing behavior and explaining code accurately | SWE-Atlas-QnA-AA: 124 tasks |
How do you make every run like-for-like?
Freeze the experimental configuration before the first run. A comparison is reproducible only when another evaluator can reconstruct the same starting state and constraints.
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- Pin the model and agent versions. Record the exact model identifier, release date or build, agent version, harness commit and reasoning setting. Do not report a moving product label without a version.
- Pin the repository. Store the repository URL, commit hash, task identifier and any fixture or dataset revision. Start each attempt from a clean copy of that commit.
- Pin the execution image. Record the container or virtual-machine image, operating-system version, language runtimes, package-lock state and preinstalled tools.
- Define tool and network policy. List every permitted tool, filesystem boundary, secret policy and network rule. Apply the same policy to every agent.
- Set the time and retry budgets. State the maximum wall-clock time, number of attempts and what happens after a timeout. A retry must start from a documented state.
- Fix sampling controls. Record temperature, seed or other sampling parameters where the system exposes them. If a setting is unavailable, say so rather than implying deterministic generation.
- Capture raw artifacts. Save prompts, tool calls, tool outputs, timestamps, patches, test logs, exit reasons and token counts. Redact secrets without deleting events needed to audit behavior.
Start and end timestamps let you calculate wall time independently of token generation. They also expose queueing or setup overhead that a model-only latency figure would miss.
How should you choose tasks that represent real software work?
Mix issue types, repository sizes, languages, test styles and failure modes. Avoid a set where every task rewards the same search-and-patch pattern. Include tasks that require reading unfamiliar code, changing multiple files, updating tests, handling command-line tools and explaining a solution.
Use held-out or private work when possible
Fresh tasks reduce the chance that a model has seen the issue, patch or discussion during training. CCBench is designed around small real-world codebases that are not part of LLM training data and reports results over roughly 180 tasks; its approach is described at CCBench. A private holdout is stronger than assuming a public issue is unseen.
Audit for contamination
- Search public repositories, issue trackers and benchmark mirrors for the task description and reference patch.
- Check whether the repository commit predates the model’s training or data cutoff where that information is available.
- Flag tasks whose solutions, tests or walkthroughs are widely indexed.
- Keep a contamination flag in the result record and report scores with flagged tasks included and excluded when the sample permits.
Check that tasks are valid
A broken test, contradictory requirement or missing dependency can turn a capable solution into a false failure. Run every task with a reference solution and a clean baseline before inviting agents. Record environmental defects separately from agent errors.
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Use layered grading. Deterministic checks establish whether the requested behavior works; transcript or tool-call checks reveal invalid actions; human or model judges handle explanations, maintainability and ambiguous requirements.
Anthropic summarizes this design as: “Agent evaluations typically combine three types of graders: code-based, model-based, and human.” The guidance is available in its 2026 evaluation research.
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Code-based grading
Run the repository’s tests plus task-specific tests that cover the requested behavior and important regressions. Prefer deterministic assertions with a clear pass/fail rule. If a test is flaky, quarantine it and report the reliability problem rather than treating intermittent success as a clean pass.
Trace and integrity grading
Inspect tool calls for prohibited network access, edits outside the workspace, fabricated test results, skipped required checks and attempts to alter the grader. A patch that passes only because the agent changed or disabled the test is not a valid success. Record the incident as a reward-hacking or integrity failure even if the final test command exits successfully.
Human or model-assisted grading
Use a rubric for maintainability, explanation quality, requirement interpretation and changes that are difficult to test automatically. Keep these judgments distinct from functional pass/fail. Have reviewers inspect the same patch and trace evidence, and preserve disagreement or confidence rather than forcing an unsupported binary verdict.
Which metrics belong in the published comparison?
Pass rate is necessary but insufficient. Report central results and the operational cost of obtaining them.
| Metric | How to report it | Why readers need it |
|---|---|---|
| Task success or pass@1 | Share of first attempts that satisfy the functional rubric; define whether retries are excluded | Shows direct task-solving ability |
| Repeat reliability | Variance or confidence interval across repeated runs on the same tasks | Shows whether a score is stable |
| Cost per task | Estimated API cost per attempt and per resolved task, with the pricing date and assumptions | Connects quality to budget |
| Token use | Input, output and tool-related tokens, reported by task or aggregate | Reveals hidden consumption |
| Wall-clock time | Median and tail duration from start to final verdict | Measures developer waiting time |
| Timeout and error rate | Separate timeouts, infrastructure errors and agent failures | Distinguishes inability from availability problems |
| Reward-hacking or integrity rate | Count and percentage of runs with prohibited or deceptive behavior | Shows process safety, not just output quality |
| Scope of changes | Files and lines changed, with unnecessary edits noted | Highlights collateral risk |
Publish mean, median and tail metrics where the sample supports them. A mean pass rate can look healthy while a small number of very slow or expensive tasks dominate real usage, so include distributions or percentiles for cost and time.
What should each task record contain?
Use a machine-readable record for every attempt, then publish an aggregate that can be audited back to those records.
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- Task ID, repository and repository commit
- Agent, model and harness versions
- Tool permissions and network policy
- Start and end timestamps
- Files changed and patch identifier
- Tests run, commands used and results
- Exit reason: pass, functional failure, timeout, infrastructure error or policy violation
- Input, output and tool token counts
- Estimated API cost and pricing date
- Wall-clock duration
- Functional grader verdict and any human or model-judge verdict
- Integrity, reward-hacking and contamination flags
Aggregate by task family before producing a composite. If you publish an overall score, show the weighting and the unweighted family results beside it. Equal weighting is a policy choice, not a property of the agents.
How do you detect reward hacking and process failures?
Design checks that make shortcuts visible. Compare the final patch with the task specification, run tests from a clean checkout and inspect the full transcript rather than only the final answer.
- Reject edits to grader code, hidden tests or configuration whose sole effect is to suppress failures.
- Flag claims that tests were run when the transcript shows no such command or shows an ignored error.
- Check for unauthorized network calls, access to host files or use of credentials outside the declared policy.
- Compare changed files with the intended scope; unexplained generated files or broad formatting changes increase review risk.
- Run independent regression tests after the agent exits, because an agent can optimize for the visible test command.
Report these incidents separately from ordinary functional failures. A system that passes by violating the rules is not equivalent to one that passes within the allowed process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do SWE-bench scores actually tell you?
SWE-bench is useful evidence about issue-resolution ability, but its score is not a complete measure of real-world coding-agent quality. SWE-bench Verified is a human-validated subset of 500 issues, as documented at SWE-bench Verified. Epoch AI’s page says the set was reviewed by 93 software developers, with each sample reviewed by three annotators; see Epoch AI’s benchmark description.
Those validation details improve confidence in task construction, but the benchmark still emphasizes a particular issue-and-patch workflow. It does not by itself measure terminal-heavy operations, repository explanation, cost, latency, safety or repeat reliability. Keep its result as one family or reference point rather than treating it as the universal ranking.
Task validity can dominate apparent model differences. In a July 8, 2026 audit, OpenAI wrote: “Through a detailed audit, we find widespread task issues in SWE-Bench Pro and estimate that ~30% of the tasks are broken.” The audit is at Separating Signal from Noise in Coding Evaluations. This estimate concerns SWE-Bench Pro, not every SWE-bench variant, so do not transfer the percentage without qualification. The practical lesson is to publish task-quality audits and sensitivity analyses.
| Benchmark fact | What it means for comparison |
|---|---|
| SWE-bench Verified: 500 issues | A validated issue-resolution sample, not a full agent-work profile |
| Verified review: 93 developers; three annotators per sample | Evidence of human review depth for that subset |
| FrontierCode v1.1 Main: 100 tasks | A software-engineering component in Artificial Analysis’s current index |
| Terminal-Bench 4.0: 66 tasks | A terminal-execution component in that index |
| SWE-Atlas-QnA-AA: 124 tasks | A repository-understanding component in that index |
| CCBench: roughly 180 tasks | A fresh-codebase approach intended to reduce training-data contamination |
| SWE-Bench Pro audit estimate: approximately 30% broken tasks | A warning that benchmark defects can distort rankings |
How can you run a reproducible evaluation from start to finish?
- Write the protocol. Define task families, inclusion criteria, graders, budgets, allowed tools, weighting and failure categories before running agents.
- Prepare clean environments. Build one pinned image per repository or language stack, verify dependencies and run the baseline tests.
- Sample and randomize. Select a held-out portfolio and randomize task order where operational effects could bias results. Keep the task list and randomization seed in the record.
- Execute identical attempts. Give each configuration the same prompt structure, repository revision, time budget, retry limit and tool policy.
- Collect artifacts automatically. Save transcripts, patches, command logs, tests, timestamps, tokens and costs while the run is active.
- Grade in layers. Run deterministic tests first, then integrity checks, then the human or model rubric for cases that require judgment.
- Review anomalies. Investigate unexplained passes, impossible timings, infrastructure errors, contaminated tasks and reward-hacking flags before aggregation.
- Publish disaggregated results. Show family-level pass rates and operational metrics, followed by any composite and its formula.
- Repeat a sample. Re-run enough tasks to estimate variance. A single pass on a stochastic agent is not a reliability estimate.
How should teams choose an agent for production work?
Match the evaluation profile to the work your team actually assigns. A repository-maintenance team should emphasize repair quality, regression tests and scope control. An automation team should weight terminal success, recovery from command failures and timeout rate. A support or onboarding team should give more weight to repository answers and trace accuracy.
Use a decision matrix instead of one leaderboard rank
- Quality gate: Set a minimum functional pass rate and reject configurations with unacceptable integrity incidents.
- Reliability gate: Require repeatability and a bounded timeout or infrastructure-error rate.
- Operational fit: Compare cost per resolved task and wall time against your budget and developer workflow.
- Coverage: Confirm that the agent performs acceptably in every task family you will assign, not just its strongest one.
- Reproducibility: Prefer a configuration whose versioning, logs and environment can be recreated when a regression appears.
Keep the raw task-level results. When a model, harness or repository changes, rerun a fixed sentinel set and compare the new distribution with the prior version instead of assuming the old score still applies.
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- Comparing model names while changing harnesses. This confounds model ability with tool and prompt engineering.
- Using only one benchmark family. A strong patch solver may be weak at shell work or code explanation.
- Reporting only pass rate. Slow, expensive or unreliable passes can be impractical in production.
- Allowing hidden retries. Readers cannot reproduce a score if the number of attempts is undisclosed.
- Ignoring task defects. Broken tests and missing dependencies create false negatives and can change rankings.
- Trusting visible tests alone. Agents can overfit or manipulate the test path; independent checks are needed.
- Publishing a composite without components. Weighting choices can hide a family where an agent fails consistently.
- Failing to preserve artifacts. Without transcripts and patches, anomalous results cannot be audited.
What is the practical standard for a credible result?
A credible comparison lets a skeptical reader answer five questions: What exact system ran? What tasks and repository revisions did it receive? What was it allowed to do? How was success judged? What did success cost in time, tokens and money? If any answer is missing, label the result accordingly and avoid presenting it as a definitive ranking.
The strongest report combines held-out real repositories, three distinct task families, pinned environments, layered graders, integrity audits and transparent operational metrics. It treats benchmark quality as part of the measurement problem and preserves enough raw evidence for reproduction. That approach produces a decision tool for real software work rather than a leaderboard number detached from engineering practice.
Frequently Asked Questions
How many tasks do you need for a useful coding-agent evaluation?
There is no universal minimum because variance depends on task diversity and stochasticity. Use enough tasks in each family to expose failures, then repeat a subset to estimate variance; publish the sample size and confidence interval rather than implying precision from a small score.
Should every agent receive the same prompt?
Use the same task specification and constraints, but document any interface-specific wrapper required to invoke each agent. If wrappers differ, treat them as part of the harness and disclose the difference instead of claiming a pure model comparison.
How do you price an agent when provider billing is complicated?
Record input, output and tool token counts, the pricing schedule date and any fixed platform or execution fees. Report estimated cost per attempt and per resolved task, and separate one-time setup costs from recurring inference costs.
What should you do when a task has flaky tests?
Measure the baseline flake rate before evaluation, isolate the unstable test and document the rule used to grade it. Do not silently count an intermittent pass as deterministic success; rerun or replace the test and keep the task-quality flag.
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