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Benchmark AI coding agents on large Python codebases by giving them realistic, multi-file tasks in complete, version-pinned repositories, then measuring more than whether tests pass. A credible comparison controls the environment and harness, checks patches against visible and hidden tests and representative workloads, and reports correctness, regressions, performance, maintainability, time, and total cost separately.
Large repositories change what a coding task demands: an agent may need to find relevant code, trace dependencies, discover tests, and reason across components before it can make a correct change. A benchmark made only of small, isolated coding exercises cannot establish how well an agent handles those challenges.
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What makes a large-repository coding benchmark fair?
A fair benchmark evaluates the whole agent system on a defined task, not just a model’s ability to produce code from a prompt. The task, repository state, available context, tools, and execution conditions all affect the result. To compare agents meaningfully, keep those conditions fixed or make each difference an explicit experimental factor.
Use a task tied to a specific repository commit and a concrete issue, failing test, feature request, security fix, refactor, or performance regression. Record the task statement, expected behavior, base tests, permitted files and tools, and an expert reference patch when one exists. A task should require investigation and a repository change—not merely completion of a self-contained function.
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Repository scale matters because the agent may have to locate the relevant implementation, follow behavior across files, identify how tests are organized, and avoid breaking unrelated features. Measure those capabilities through realistic tasks and diagnostic results; do not infer them from a single overall pass rate.
Define the benchmark contract before a run
Write a short contract for every task. It should state the starting commit, the user-visible problem, acceptance behavior, commands the agent may run, files or directories that are off limits, time and resource limits, and what constitutes a submitted patch. Distinguish an allowed code change from an allowed evaluation action: an agent may be permitted to run the public test suite while remaining barred from private tests, held-out task files, or network access.
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How should you choose tasks for a Python repository?
Build a mix that reflects the work done in the repository. Real issues, pull requests, and production or community workloads provide stronger grounding than prompts written solely to be easy to grade.
Include different kinds of engineering work
- Bug fixes: Start from a reproducible defect and specify the expected behavior.
- Feature additions: Test whether an agent can implement a behavior that fits existing interfaces and conventions.
- Refactors: Check that structure changes preserve existing behavior and remain compatible.
- Dependency or API migrations: Evaluate changes across call sites, tests, and integration boundaries.
- Test repairs: Distinguish a legitimate test fix from changing or weakening a test to hide a defect.
- Performance work: Require a representative workload and a performance target, alongside correctness checks.
Sample tasks across core libraries, command-line interfaces, services, data pipelines, and integration boundaries where applicable. Include different task sizes and difficulty levels; otherwise, a benchmark can overstate performance by concentrating on one part of the codebase or one kind of change.
Use existing benchmark collections with their scope in mind
SWE-fficiency evaluates tasks on complete codebases, asking agents to investigate semantics, locate bottlenecks and relevant tests, and produce a patch that passes the tests while meeting an expert speedup target. Its 2026 benchmark includes 498 tasks across nine repositories. The authors report that top agents average less than 0.23x the expert speedup on their tasks. That result describes this benchmark’s performance tasks, not a general success rate for all Python development.
FormulaCode, published in 2026, mines 957 performance bottlenecks from scientific Python repositories and pairs them with expert patches. It reports an average of 264.6 community-maintained workloads per task. Those real workloads can support performance evaluation in a way that a synthetic timing example may not, but the result still depends on which workloads a benchmark selects and how it executes them.
SWE-PolyBench contains 2,110 instances from 21 repositories and covers bug fixes, features, and refactoring across multiple languages, including Python. Its aggregate results should not be presented as a measurement of large-Python performance alone. CodeScaleBench, published in 2026, compares matched tasks with and without MCP context retrieval across 20 suites; it can inform retrieval comparisons, but does not replace task selection and environment controls for a particular Python codebase.
Balance task difficulty and repository coverage
Stratify the sample before agents run. Record repository area, number of files an expert patch changes, estimated difficulty, and whether the task crosses package or service boundaries. Include both localized changes and changes that require tracing a call path through several modules. Keep a held-out set for final comparison rather than repeatedly tuning prompts on every task.
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For performance tasks, preserve the original bottleneck and workload definition. FormulaCode’s 957 bottlenecks and average of 264.6 community workloads per task illustrate why a realistic workload matters: a fast result on one tiny input can be irrelevant to the behavior users experience. Ask for an expert speedup target when one exists, but also require unchanged functional behavior and report cases where an agent optimizes the benchmark input at the expense of generality.
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What should you pin before running agents?
Freeze the conditions that can change the outcome. At minimum, record the repository commit, Python version, operating-system image, dependencies and lockfile, test data, hardware class, timeout, model version, agent configuration, tool permissions, and random seeds. Keep private tests and held-out tasks inaccessible to the agent.
| Condition | What to record or control | Why it matters |
|---|---|---|
| Repository and dependencies | Commit, Python version, OS image, dependency lockfile, and test data | Defines the code and runtime against which the patch is evaluated. |
| Agent and model | Model version, configuration, prompt, tool permissions, and random seed | Enables an apples-to-apples comparison and makes stochastic behavior visible. |
| Execution environment | Hardware class, isolation method, timeout, and workload warm-up policy | Constrains resource access and makes timing conditions interpretable. |
| Task and evaluation | Task statement, base tests, hidden-test handling, allowed tools, and reference patch when available | Clarifies what the agent was asked to do and how the result will be judged. |
Run agents in isolated containers or virtual machines so each trial starts from the same repository state and cannot alter another run’s environment. Define the timeout and tool permissions before testing. If a task is stochastic, repeat it or report confidence intervals; a single run can hide variability.
Keep the agent’s access to evaluation data consistent with the benchmark’s purpose. It may need ordinary repository tests to work effectively, but private tests and held-out tasks should not be available for prompt-specific tuning. Record what the agent can read, change, execute, and access over the network or through other tools.
Make timing and data handling reproducible
State whether dependencies are installed from a warm cache, whether services are started afresh for each trial, and how many warm-up iterations precede a measured workload. Use the same test data and hardware class for every agent. Save environment manifests and command output, not just a final score, so a later rerun can distinguish a code change from a machine or dependency change.
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How do you account for retrieval and the agent harness?
Retrieval and the harness are part of the system being evaluated, not incidental details. To measure their effect, run the same task under controlled conditions while holding the model, prompt, timeout, and task constant.
Possible retrieval conditions include the full repository, curated context, search tools, MCP tools, or no retrieval. Define each condition precisely: for example, specify which files are exposed or which tools can search the codebase. Otherwise, a result attributed to retrieval may actually reflect a different prompt, permission set, or time budget.
Databricks reported in 2026 that its internal benchmark used real engineer tasks on a multi-million-line codebase and found that harness choice dramatically affected cost and quality. The benchmark was internal and multi-language, not a public Python-only comparison, so its findings are a caution about evaluation design rather than a universal estimate for Python agents. Its central practical lesson is that token price alone is a poor proxy for end-to-end task cost: the harness can change how much work an agent does and whether that work succeeds.
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When comparing retrieval setups, preserve full logs and label each run by retrieval condition and harness. This lets readers tell whether an agent’s result came from the model, the context made available, or the way the system orchestrated tools and retries.
Compare harness behavior, not just model names
Document planning prompts, context-window limits, repository indexing, command approval rules, retry policy, patch application method, and stopping criteria. Two systems using the same model can have very different trajectories if one automatically summarizes files, retries failed commands, or supplies MCP context. A fair comparison either standardizes these features or reports them as separate harness conditions.
Measure retrieval precision and recall when the setup supports it: which files or symbols were surfaced, which relevant locations were missed, and how much irrelevant context was supplied. SWE-PolyBench’s language, complexity, retrieval, and resolve-rate views show the value of such diagnostic slices. Retrieval metrics do not replace behavioral tests, but they can explain why an otherwise capable agent never reached the code that needed changing.
How should you score an agent’s patch?
Use separate measures for correctness, safety, performance, patch quality, process, and cost. A green test suite is necessary evidence, but it does not establish that a change is fast, maintainable, or free of regressions.
Functional correctness and regression safety
Run visible tests and private or held-out tests where available. Include regression tests for existing behavior, and preserve the base test results so failures can be compared with the starting commit. Add type checks, lint, security checks, and backward-compatibility checks when they fit the repository. Report each category separately rather than treating one passing command as proof of quality.
Check whether the patch changes or removes tests in ways that make the evaluation less meaningful. A patch can produce a passing suite by weakening an assertion or bypassing the intended behavior. Review the changed tests and, where possible, use hidden tests to check behavior beyond the agent-visible examples.
Distinguish a genuine regression from a pre-existing failure. Run the same evaluation commands on the untouched commit, record expected failures, and compare the patched result with that baseline. A useful regression rate counts newly introduced failures, not every failure observed in a repository with an imperfect starting suite.
Performance and resource effects
For performance tasks, run representative workloads under a stated, repeatable policy. Measure runtime and, where relevant, memory and throughput. Compare with the baseline and an expert reference when available, and report the exact workload and conditions rather than a bare speedup number. A performance improvement that breaks correctness does not count as a successful patch.
SWE-fficiency combines execution validation, coverage tooling, relevant unit tests, and expert speedup baselines. FormulaCode evaluates triage, diagnosis, and resolution with multiple metrics rather than reducing performance work to a binary pass or fail. These approaches help separate finding the bottleneck from implementing and validating a useful fix.
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Report both absolute measurements and relative change. Include warm-up policy, input size, number of repetitions, and whether caches or external services were involved. Memory growth or throughput loss can make a faster wall-clock result unacceptable, so keep those dimensions visible rather than folding them into a single speedup figure.
Patch quality and engineering review
Record changed files and lines, test additions, and complexity or duplication changes where those measures are available. Pair automated measures with a reviewer rubric for clarity, scope, consistency with repository conventions, and unnecessary changes. These signals describe maintainability; they should not be disguised as an objective single measure if they depend on human judgment.
Inspect API compatibility and configuration behavior when a task crosses package boundaries. Note generated files, migration scripts, and documentation changes separately from production-code edits. A small patch that leaves a required migration undone is not higher quality merely because it changes fewer lines.
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Process and cost
Capture the trajectory: elapsed time, time to first viable patch, retries, tool calls, failed commands, and trajectory length. Record model tokens and any tool or sandbox costs, then calculate total task cost using a stated accounting method. The amount a model charges per token does not capture the cost of repeated attempts, longer runs, or infrastructure.
A transparent cost record can separate input tokens, output tokens, tool calls, retry overhead, sandbox or VM time, and any paid retrieval or storage service. Report cost for every task and summarize median and tail values; a low average can hide a small number of extremely expensive failures. If infrastructure is shared, state the allocation rule used to assign its cost.
Keep correctness, regressions, performance, patch quality, process, and cost as distinct scorecard fields. If you need a single ranking, decide the weights before running the benchmark and publish them. A weighted score can be useful for a specific decision, but it should not conceal whether an agent is cheaper because it is less successful or faster because it makes riskier changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should benchmark results be reported?
Publish the overall solve rate alongside the breakdowns that explain it. A single average can hide that an agent succeeds on small bug fixes but struggles with migrations, or performs well in one repository and poorly in another.
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Useful slices include repository, task family, difficulty, patch size, changed-file count, retrieval condition, harness, model, timeout, and total cost. Report hidden-test pass rate, regression rate, performance speedup, memory impact, elapsed time, retries, tool calls, and patch-quality measures where applicable. State the denominator and task inclusion rules for each rate.
Use distributions, not only means. Median time and cost show a typical task, while percentile values expose tail behavior that matters in production. For stochastic agents, include the number of independent runs and confidence intervals or another clearly described uncertainty measure.
SWE-PolyBench exposes views by language, complexity, retrieval precision and recall, and resolve rate, illustrating why diagnostic breakdowns matter. For your own benchmark, explain how each category is defined and avoid comparing unlike task sets as though they were interchangeable.
Include the repository commit, dependency lockfile, hardware, timeout, tool permissions, model configuration, random seeds, workload warm-up policy, full patch, and trajectory logs. This metadata allows readers to interpret the result and, where possible, reproduce it. Benchmark task counts, leaderboards, model prices, and repository versions can change, so date claims and identify the benchmark version or publication year.
Make comparisons auditable
Provide a task manifest with stable identifiers, repository and commit, task family, difficulty label, visible-test command, hidden-test status, and reference-patch availability. Publish patch diffs and machine-readable result records where policy permits. Explain exclusions such as infrastructure crashes, missing dependencies, or tasks that were invalid at the starting commit. Without these rules, a reported solve rate can change simply because failed or expensive cases were omitted.
What common mistakes make results misleading?
- Using only small, isolated exercises: These do not test repository navigation, dependency tracing, or multi-file reasoning.
- Reporting only test pass rate: Passing tests can coexist with performance regressions, weak maintainability, or high cost.
- Changing several conditions at once: A new model, prompt, harness, and retrieval setup make it impossible to attribute a result to one factor.
- Relying on one run for variable tasks: Repetition or confidence intervals are needed to communicate stochastic variation.
- Publishing only an overall average: Without repository and task-family slices, readers cannot see where an agent succeeds or fails.
- Calling a multi-language result Python-specific: Multi-language collections such as SWE-PolyBench can include Python but do not establish a pure large-Python estimate.
- Using token price as total cost: Retries, tool use, runtime, and sandbox infrastructure can materially affect the bill.
- Allowing test leakage: If private tests or held-out tasks are visible, a score may measure adaptation to the evaluation rather than general task-solving ability.
- Mixing baseline failures with regressions: Count failures introduced by the patch separately from failures already present at the starting commit.
- Timing without a workload protocol: Unstated warm-up, cache, input-size, or hardware differences can make speedup claims meaningless.
- Rewarding unnecessary edits: A patch that changes unrelated files or weakens tests may pass while increasing maintenance risk.
Frequently Asked Questions
Should I use SWE-bench to evaluate agents on my monorepo?
A benchmark collection can provide task ideas and a point of comparison, but it cannot substitute for tasks and tests grounded in your repository. The cited evidence here does not establish a universal monorepo-specific threshold or say that one suite alone represents every large Python codebase.
How many times should I run each coding-agent task?
There is no universal repeat count established by the cited benchmark evidence. Choose a repeat policy appropriate to the variability and cost of your setup, then disclose it or report confidence intervals so readers can judge how stable the results are.
Can an agent use hidden tests during a benchmark?
Not if those tests are intended to serve as an independent evaluation. Keep them unavailable to the agent and document what test commands and files it could access during the run.
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What is a good pass-rate threshold for a coding agent?
The cited sources do not establish a universal quality threshold. A useful target depends on the task mix, repository risk, and the relative importance of correctness, regressions, performance, and cost.
How do I measure whether an agent made the codebase worse even when tests pass?
Compare the patch with the baseline using hidden tests, type and lint checks, security and compatibility checks, representative workloads, memory and throughput measurements, complexity or duplication deltas, and a review of changed tests and unrelated files. Report these dimensions separately from functional pass rate.
Should retrieval be part of the benchmark or held constant?
Treat retrieval as an explicit factor. Hold model, prompt, task, and timeout constant, then compare full-repository, curated-context, search-tool, MCP, or no-retrieval conditions with precise permissions and complete trajectory logs.
What belongs in total task cost?
Use a stated accounting method that includes model input and output tokens, retries, tool calls, sandbox or VM time, and paid retrieval or infrastructure services. Token price alone does not represent end-to-end cost.
How should I evaluate a performance optimization?
Require the patch to preserve functional behavior, run a representative workload under a fixed warm-up and measurement policy, and report runtime, memory, throughput, baseline comparison, and expert-relative speedup when available.
The Bottom Line
A defensible benchmark gives every agent the same pinned repository and task conditions, evaluates patches with visible and hidden tests plus realistic workloads, and reports correctness, regressions, performance, maintainability, process, and total cost as separate results.
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