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The fairest way to compare AI coding agents is to evaluate the complete agent system—model, harness, tools and settings—on a reproducible portfolio of real repository repairs, terminal workflows and code-understanding questions. Pin every version and permission, run identical tasks from fixed commits, grade behavior with deterministic tests, then report pass rate alongside cost, tokens, elapsed time, reliability and process failures. A single leaderboard score cannot show whether an agent is dependable in your codebase.
What does a fair comparison of AI coding agents measure?
A comparison should answer a practical question: which agent completes the software work you actually assign, under the constraints you actually impose? That means measuring more than whether a patch eventually passes one test. The unit being compared is an agent–model–harness combination, not an isolated model name. The harness determines how the model sees the repository, which tools it can call, whether it can retry, and how failures are handled.
Use a portfolio that exercises three distinct abilities:
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Repository repair and feature work: change an existing codebase while preserving its behavior and conventions.
- Terminal-heavy execution: navigate files, install or inspect dependencies, run commands, diagnose failures and complete an end-to-end task.
- Repository question answering and code tracing: locate behavior, follow a call path and explain what the code does without making unnecessary edits.
These families should remain visible in the results. Artificial Analysis uses an equally weighted index built from FrontierCode v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA-AA because agents can perform very differently by task family. Its methodology lists 100 FrontierCode v1.1 Main software-engineering tasks, 66 Terminal-Bench 4.0 tasks and 124 SWE-Atlas-QnA-AA tasks. Read the methodology for the current composition and weighting.
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Which task families should you include?
How should repository repair and feature tasks be designed?
Start from a real repository revision and give the agent a user-visible issue, a feature request or a failing behavior. The task should require edits in the normal project structure, not a single isolated function chosen to make patching easy. Include the repository’s existing tests and any setup instructions a developer would reasonably receive.
Define success before the run. A passing solution should satisfy the requested behavior, preserve relevant existing behavior and meet project-level checks such as linting or type validation when those checks are part of the repository workflow. Record which files changed and whether the agent modified tests, configuration or unrelated code.
What makes a terminal-heavy task realistic?
Terminal tasks test operational competence that a patch-only benchmark can miss. Ask the agent to inspect an unfamiliar checkout, reproduce a failure, use the available command-line tools and leave a working result. Keep the environment deterministic: use a fixed container image, controlled dependencies and a declared network policy. If network access is disabled, state that before comparing agents; silently giving one agent broader access invalidates the result.
Examples include diagnosing a build or test failure, migrating a configuration, repairing a script that interacts with several services mocked inside the container, or updating a project whose correct fix requires multiple commands. Grade the final artifact and the command trace. An agent that reaches a green test suite by deleting tests or bypassing a required check has not completed the task correctly.
How do repository Q&A and code-tracing tasks differ?
Q&A tasks measure code comprehension without conflating understanding with editing speed. Ask for a precise explanation of where a behavior is implemented, which conditions trigger it, or how data moves through a subsystem. Require file and symbol references so the answer can be checked against the pinned commit.
These tasks expose agents that can generate plausible code but cannot build an accurate mental model of an existing system. Score factual correctness, coverage of the requested path and whether the answer distinguishes confirmed behavior from an inference.
How do I make every run like-for-like?
Write a configuration manifest and freeze it before running the comparison. The same task can produce a different score when any of these variables changes:
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- Agent and model release, including the exact build or API version.
- Harness version, system prompt, context-window settings and reasoning configuration.
- Tool set, shell implementation, filesystem scope and write permissions.
- Network policy, credentials, environment variables and available services.
- Repository commit, submodule state, operating-system image and dependency lockfiles.
- Time limit, maximum turns, retry budget, temperature and other sampling settings.
Publish the manifest with the results. A score without it is not reproducible and should not be presented as a property of the model alone. If an agent is allowed to retry, report the retry policy explicitly: a first-attempt score and a score after recovery answer different operational questions.
Use fresh isolated workspaces for each run. Reset the repository to the same commit, clear generated artifacts and capture timestamps. Store the exact prompt and any task-specific files. If a run is interrupted, classify the exit reason rather than silently dropping it from the denominator.
How should I assemble a representative task set?
Balance the set across task families, languages, repository sizes and difficulty. Do not let a large number of easy unit-test fixes drown out a smaller but important group of integration tasks. Keep a private evaluation split for decisions and a public or development split for harness debugging.
Prefer tasks created after the models’ likely training period, newly written tasks, or private internal issues. CCBench is an example of this principle: it targets small real-world codebases that are not part of LLM training data and reports results over roughly 180 tasks. See CCBench’s task description.
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How do I run an evaluation without contaminating the result?
- Prepare the environment. Build one container or virtual-machine image, install locked dependencies and verify that the baseline repository is in the expected state.
- Assign an immutable task identifier. Store the repository URL or internal source reference, commit hash, task text and acceptance tests together.
- Launch isolated runs. Give each agent the same starting files, permissions, time limit and network policy. Record start and end timestamps automatically.
- Capture the complete trace. Save prompts, responses, tool calls, command output, files changed, tests run and exit reason. Redact secrets before publication.
- Run the authoritative grader. Execute deterministic tests in a clean post-run workspace, not only the tests the agent chose to run.
- Apply secondary checks. Inspect the trace and diff for prohibited actions, test deletion, hard-coded answers, unrelated changes or other reward-hacking behavior.
- Repeat where reliability matters. Use repeated samples or repeated runs with the declared sampling settings, and retain every failure in the analysis.
Do not let an agent edit the grader, acceptance tests or evaluation metadata. Mount those assets read-only or run grading in a separate environment. If the task legitimately requires test changes, review the diff and evaluate the original acceptance behavior independently.
What grading system gives a trustworthy result?
Use layered grading. Anthropic describes the usual pattern this way: “Agent evaluations typically combine three types of graders: code-based, model-based, and human.” Anthropic’s evaluation guidance explains why the layers complement one another.
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When should code-based graders decide?
Deterministic tests should decide functional success whenever the requirement can be expressed as an assertion. Run the project’s existing tests plus targeted hidden tests for the requested behavior. Keep the test command and version fixed, and distinguish a genuine test failure from an infrastructure error.
Report a task as passed only when the required checks pass and the patch satisfies integrity rules. A green test suite is not sufficient if the agent removed the test, skipped the code path or altered the grader.
What should trace and tool-call checks catch?
Automated trace checks can flag invalid tool calls, commands outside the allowed scope, network-policy violations, attempts to read secrets and edits to protected files. They can also detect suspicious patterns such as replacing a real implementation with a constant that matches visible tests.
Keep these findings separate from functional failure. An agent may produce a passing patch with a process violation; combining both into one binary label hides a safety or governance problem.
When are model or human judges necessary?
Use a model judge or trained human reviewer for explanations, maintainability, documentation quality and requirements that are inherently ambiguous. Provide the judge with the task, the pinned repository revision, the final diff and the relevant trace—not merely the agent’s self-description. Calibrate reviewers with example decisions and record disagreement rather than forcing false precision.
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Pass rate is necessary but insufficient. Publish the following for the full set and for each task family:
| Metric | What it tells the reader | How to report it |
|---|---|---|
| Task success or pass@1 | Whether the first allowed attempt met the acceptance contract | Count and percentage, with the denominator and test definition |
| Repeat reliability | How consistently the agent succeeds under the same setup | Per-task variance, confidence interval or repeated-run distribution |
| Cost per task | Operational spend required to obtain an attempt or a resolved task | State the pricing date, token accounting and whether failed runs are included |
| Token use | How much model output and context the agent consumed | Input and output tokens, plus any tool or cached-token treatment |
| Wall-clock time | Latency from launch to completion or timeout | Median and tail values, with the time limit |
| Timeout and error rate | How often the system fails to finish for operational reasons | Separate timeout, infrastructure and agent-error categories |
| Reward-hacking or integrity incidents | Whether a passing result was obtained through prohibited behavior | Count incidents and describe the rule that was violated |
Also report the scope of repository changes, test quality, terminal and tool competence, code comprehension and harness reproducibility. A compact aggregate can be useful for a business decision, but never replace the family-level results with it. Include medians and tail metrics as well as means; one unusually slow or expensive run can matter more than an average.
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How should I analyze retries, variance and confidence?
Define the estimand before you run the agents. “Pass@1” means the first attempt; “pass after two retries” measures a different workflow and should not be presented as the same capability. If your production process permits retries, publish both the initial success rate and the success rate under that exact retry budget, along with the extra tokens, cost and time.
Use confidence intervals or an equivalent uncertainty estimate, especially when the task set is small. Show the number of tasks behind every percentage and break uncertainty out by family. A two-point difference on a small set may not justify a ranking, while a consistent cost or timeout difference can still affect a deployment decision.
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Preserve per-task outcomes. Aggregate scores can conceal a specialist that excels at terminal work but fails repository questions, or an agent that succeeds only on a narrow language mix. A decision-maker should be able to filter results by repository, language, difficulty and failure category.
How do I detect reward hacking and other process failures?
Design the grader so that the easiest shortcut is not the winning strategy. Review whether the agent:
- Removed, weakened or skipped tests instead of fixing the implementation.
- Hard-coded visible examples, expected outputs or task-specific paths.
- Changed unrelated files, generated artifacts or evaluation scripts.
- Used network access, credentials or tools outside the declared policy.
- Stopped after a superficial command success without validating the requested behavior.
- Reported tests as passing when the captured output shows a different command or failure.
Run hidden tests and inspect the final diff in a clean checkout. Where safety matters, treat any prohibited action as a separately reported incident even if the functional tests pass. This preserves the distinction between “works” and “works in an acceptable way.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can I trust SWE-bench scores?
Use SWE-bench as one signal, not as a universal ranking of coding agents. SWE-bench Verified is a human-validated subset of 500 issues. Its documentation is available at swebench.com/verified.html. Epoch AI says the subset was reviewed by 93 software developers, with each sample reviewed by three annotators; see its benchmark description.
Benchmark quality can dominate apparent model differences. In a July 8, 2026 audit, OpenAI estimated that approximately 30% of SWE-Bench Pro tasks were broken. The audit states: “Through a detailed audit, we find widespread task issues in SWE-Bench Pro and estimate that ~30% of the tasks are broken.” Read the audit for the task-quality findings.
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Before using any leaderboard number, check the benchmark version, task validity process, agent harness, tool permissions, retry policy and whether the score is pass@1 or an undisclosed aggregate. Reproduce a sample on your own held-out tasks; a public score cannot establish performance in a repository, language or workflow it does not cover.
What should a reproducible scorecard contain?
Keep one record per task and make the raw records auditable. At minimum, capture:
- Task ID and repository commit.
- Agent, model and harness versions.
- Tool permissions and network policy.
- Start and end timestamps, time limit and retry count.
- Files changed, tests run and test results.
- Exit reason: pass, functional failure, timeout, infrastructure error or policy violation.
- Input and output tokens and estimated API cost.
- Wall-clock time.
- Grader verdict and any human or model-judge notes.
- Integrity, contamination or task-validity flags.
Aggregate these records by task family, then publish the distributions and the mean, median and tail values that matter to your workflow. Keep invalid or broken tasks visible in a separate validity report; removing them without explanation can make a benchmark appear more stable than it is.
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Start with non-negotiable constraints. If your environment forbids network access, eliminate configurations that require it. If latency is critical, set a maximum wall time and compare the fraction of tasks completed inside that limit. If code review capacity is scarce, weight maintainability and unrelated-change incidents instead of selecting solely on pass rate.
- Set minimum gates. Require a target functional pass rate, zero tolerance for specified policy violations and an acceptable timeout or infrastructure-error rate.
- Compare by task family. Match the agent’s strengths to the work your team actually performs rather than relying on one composite score.
- Calculate resolved-task cost. Include failed attempts, retries, reviewer time where measured and the tokens spent on tasks that never pass.
- Inspect representative traces. Review both successful and failed runs for debugging quality, unnecessary edits and recovery behavior.
- Validate on a private holdout. Keep a set of fresh internal tasks out of tuning and rerun it when the agent or harness changes.
Re-run the evaluation after model, harness, tool or repository changes. A new score is not directly comparable unless the configuration and task validity remain aligned. Preserve prior manifests so improvements and regressions can be attributed to a specific change.
What are the most common comparison mistakes?
- Comparing model names instead of systems: different wrappers, prompts, tools and retry budgets can reverse the outcome.
- Publishing one blended number: it hides whether an agent is a repair specialist, a terminal operator or a strong code explainer.
- Counting only green tests: test deletion, grader edits and policy violations can produce deceptive passes.
- Ignoring cost and latency: a slightly higher pass rate may not justify substantially greater spend or queue time.
- Dropping timeouts and errors: excluding unfinished runs inflates reliability.
- Using stale or contaminated tasks: memorized solutions and broken issues measure benchmark exposure rather than generalization.
- Changing the environment mid-study: dependency updates, network access or a different container can invalidate the comparison.
- Overinterpreting tiny gaps: without uncertainty estimates and per-task records, close scores may be indistinguishable.
A rigorous comparison is therefore a small experimental system: controlled inputs, observable actions, independent grading and an explicit accounting of uncertainty and cost. That discipline is what turns an impressive demo or leaderboard position into evidence you can use for real software work.
Frequently Asked Questions
How many tasks do I need to compare coding agents?
There is no universal minimum because the useful number depends on how diverse your repositories and task families are. Use enough tasks to cover the failure modes you care about, keep family-level counts visible, and report uncertainty rather than implying that a small convenience sample is definitive.
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Should every agent get the same system prompt?
Keep the evaluation contract and task instructions equivalent, but document each product’s required harness prompt or setup rather than silently stripping it away. If prompts differ, treat that prompt-plus-agent configuration as the object being compared and publish the exact text.
How can I compare agents that use different tokenization or caching?
Report the provider’s input and output token counters separately and state how cached or tool-generated tokens are billed. Cost per completed task and wall time remain comparable operational measures even when raw token units are not identical.
What should I do when a benchmark task is broken?
Mark the task invalid, preserve the original record and explain the validation rule that failed. Publish results with and without invalid tasks only when both denominators are clear, and replace the task with a fresh held-out item for future runs.
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