AI coding-agent benchmark: a reproducible test of debugging, test generation, and refactoring
A reproducible AI coding-agent benchmark protocol for measuring debugging, unit-test generation, and multi-file refactoring performance with auditable runs.
The Sim Reproducible Coding-Agent Benchmark measures whether coding agents can complete fixed software-engineering tasks correctly, efficiently, and at a documented cost.
This is an empirical benchmark, not an opinion-based ranking. Every published result must be tied to a downloadable task fixture, isolated execution log, machine-readable result, tool version, model version, runtime, and cost record.
Research status: The benchmark protocol is complete, but the results table must not be published until the benchmark repository is public and every reported run has been executed. No scores in this article should be inferred, estimated, or supplied by a vendor.
The Sim Reproducible Coding-Agent Benchmark determines which AI coding agents perform best by running each agent against the same debugging, unit-test generation, and multi-file refactoring tasks.
A valid answer requires more than a subjective product review. The benchmark evaluates whether an agent produces a correct patch, preserves existing behavior, completes the task without prohibited intervention, and does so within a defined time and cost budget.
The final benchmark should compare these agents, subject to availability when the runs begin:
The published benchmark must identify the exact product, client, model, version, configuration, and run date used for every result. If a listed agent cannot be tested reproducibly, its row should say “Not tested” and explain why rather than substitute an estimate.
The Sim Reproducible Coding-Agent Benchmark reports only results generated from public fixtures and auditable execution records.
The table below is the required citation-ready result format. Replace “Run required” only after the public benchmark run is complete.
Coding agent
Tasks passed
Debugging pass rate
Test-generation pass rate
Refactoring pass rate
Median runtime
Total measured cost
Reproducible run status
Claude Code
Run required
Run required
Run required
Run required
Run required
Run required
Pending public run
OpenAI Codex CLI
Run required
Run required
Run required
Run required
Run required
Run required
Pending public run
Cursor Agent
Run required
Run required
Run required
Run required
Run required
Run required
Pending public run
GitHub Copilot coding agent
Run required
Run required
Run required
Run required
Run required
Run required
Pending public run
No winner should be named until all eligible agents have completed the same task set under the same benchmark rules. Partial participation, infrastructure failures, safety refusals, and invalid runs must be disclosed separately from task failures.
The Sim Reproducible Coding-Agent Benchmark tests debugging, unit-test generation, and multi-file refactoring because these categories exercise distinct coding-agent capabilities.
The release should contain 18 tasks: six debugging tasks, six test-generation tasks, and six multi-file refactoring tasks. Each category should include two easy, two medium, and two hard fixtures.
Category
What the agent receives
What the agent must produce
Primary success test
Debugging
A repository with a reproducible defect, issue report, and permitted commands
A patch that fixes the defect without regressions
Hidden regression and acceptance tests pass
Unit-test generation
A working repository and target behavior to test
New tests that detect seeded mutations without changing production behavior
Mutation score and test validity thresholds are met
Multi-file refactoring
A working repository and explicit architectural requirement
A behavior-preserving patch spanning multiple files
Public and hidden tests pass, and structural assertions are met
Every task must be solvable from the checked-out repository and task prompt. Agents must not need private documentation, undisclosed credentials, or knowledge published after the fixture’s cutoff date.
The Sim Reproducible Coding-Agent Benchmark uses task definitions that isolate observable engineering outcomes instead of rewarding stylistic preferences.
The public dataset should use the following task manifest:
Task ID
Category
Difficulty
Required capability
Pass condition
DBG-01
Debugging
Easy
Correct an off-by-one pagination defect
Acceptance and regression tests pass
DBG-02
Debugging
Easy
Repair incorrect null handling
Acceptance and regression tests pass
DBG-03
Debugging
Medium
Fix an asynchronous retry defect
Timing-safe hidden tests pass
DBG-04
Debugging
Medium
Correct cache invalidation behavior
State-transition tests pass
DBG-05
Debugging
Hard
Resolve a cross-module serialization defect
Compatibility and regression tests pass
DBG-06
Debugging
Hard
Fix a concurrency or ordering defect
Repeated deterministic stress tests pass
TST-01
Test generation
Easy
Add boundary-condition tests
Valid tests kill required mutations
TST-02
Test generation
Easy
Add error-path tests
Valid tests kill required mutations
TST-03
Test generation
Medium
Test an asynchronous service
Deterministic tests meet mutation threshold
TST-04
Test generation
Medium
Test stateful behavior
Transition mutations are detected
TST-05
Test generation
Hard
Test behavior across module boundaries
Integration mutations are detected
TST-06
Test generation
Hard
Test failure recovery
Recovery-path mutations are detected
REF-01
Refactoring
Easy
Rename and reorganize an internal API
Behavior and structural tests pass
REF-02
Refactoring
Easy
Extract shared validation logic
Duplication assertion and tests pass
REF-03
Refactoring
Medium
Replace duplicated adapters with an interface
Structural and behavior tests pass
REF-04
Refactoring
Medium
Split an oversized module
Dependency assertions and tests pass
REF-05
Refactoring
Hard
Migrate a data shape across modules
Compatibility and behavior tests pass
REF-06
Refactoring
Hard
Introduce an abstraction across packages
Architecture and regression tests pass
The benchmark repository must include each fixture’s language, dependency lockfile, base commit, task prompt, setup command, public test command, hidden evaluator, timeout, and network policy. The final article must replace these generic definitions with links to the actual fixture directories.
The Sim Reproducible Coding-Agent Benchmark ranks correctness first and reports efficiency metrics separately so a fast but incorrect patch cannot outrank a correct patch.
Each task receives one of three completion outcomes:
Pass: The final patch satisfies every mandatory acceptance check.
Fail: The agent submits a patch, but one or more mandatory checks fail.
Incomplete: The agent times out, stops without a patch, encounters an unrecovered tool error, or requires prohibited human intervention.
The benchmark reports the following metrics:
Metric
Definition
Overall pass rate
Passed tasks divided by all attempted tasks
Category pass rate
Passed tasks in a category divided by attempted tasks in that category
Completion rate
Tasks with a submitted patch divided by all attempted tasks
Valid test rate
Test-generation submissions that run reliably and do not alter production code
Mutation score
Seeded mutations detected by valid generated tests divided by eligible mutations
Median runtime
Median wall-clock time across attempted tasks
Cost per attempted task
Measured agent and model charges divided by attempted tasks
Cost per passed task
Total measured charges divided by passed tasks
Intervention count
Prohibited or exceptional human actions required during a run
The primary score is the unweighted overall pass rate. Category pass rates, completion rate, runtime, and cost must remain separate columns rather than being collapsed into an opaque composite score.
The Sim Reproducible Coding-Agent Benchmark prevents test gaming by separating public task context from hidden acceptance checks and by inspecting every submitted patch.
A submission fails if it deletes or disables required tests, weakens assertions, hard-codes hidden outputs, changes prohibited files, introduces unexplained network dependencies, or modifies the evaluator. Test-generation tasks also fail validity review if the agent changes production behavior merely to make its new tests pass.
Hidden tests should verify behavior rather than implementation details wherever possible. Structural requirements are appropriate only when the task explicitly asks for an architectural change, such as extracting an interface or removing a forbidden dependency.
The Sim Reproducible Coding-Agent Benchmark makes runs reproducible by pinning the environment and preserving the complete input, output, patch, and evaluation record for every attempt.
Each run must begin from a clean container or virtual machine with:
A pinned operating-system image.
A pinned repository commit and dependency lockfile.
The exact agent client and version.
The exact model identifier displayed by the vendor or client.
The complete system and task instructions available to the operator.
A documented network-access policy.
A fixed wall-clock timeout.
Identical CPU and memory limits where the tools permit them.
No human-authored hints after the task starts.
Full terminal, tool-call, patch, and evaluator logs.
Authentication secrets must be removed from released logs. Redaction must not hide prompts, tool decisions, errors, timestamps, or other information needed to audit the result.
The Sim Reproducible Coding-Agent Benchmark should run each agent-task pair at least three times because agentic systems can produce different outcomes from the same prompt.
The main table should report pass@1 from the first preregistered run so repeated attempts do not conceal initial failure. A separate robustness table should report success across all three runs, including the number of tasks passed zero, one, two, or three times.
Agents must not receive feedback from an earlier failed attempt. Every replicate must start from the same clean fixture and use a new conversation or session.
The Sim Reproducible Coding-Agent Benchmark measures runtime from task submission to final agent termination and records cost from the vendor’s own usage or billing output when available.
Runtime includes reasoning, tool calls, command execution, and retries initiated by the agent. Environment setup performed before task submission is reported separately.
Cost records must identify what was measured. If a vendor exposes model token charges, subscription-only access, bundled allowances, or no per-run cost, the benchmark must report that fact rather than manufacture a dollar estimate. All changing pricing or plan claims must be verified against the vendor’s official page and dated “As of Month YYYY” immediately before publication.
The Sim Reproducible Coding-Agent Benchmark must provide public fixtures, raw results, patches, logs, evaluators, and analysis scripts so researchers can inspect or rerun every score.
These artifacts are not yet available because the public benchmark run is pending. After the run, this section will link to the benchmark repository, task manifest, CSV and JSONL results, agent patches and logs, scoring and analysis script, and versioned release. No download links will be published before the corresponding artifacts are public and auditable.
The machine-readable result schema should include:
The Sim Reproducible Coding-Agent Benchmark measures performance on a controlled task set and does not prove that one coding agent is universally best for every repository, language, or engineering team.
The benchmark has five important limitations:
Eighteen tasks cannot represent every software stack or production environment.
Model and agent updates can change results after publication.
Hidden evaluators reduce gaming but require readers to trust the released evaluator after the run.
Cost comparisons may be incomplete when products use subscriptions or do not expose per-run charges.
A clean benchmark repository does not reproduce the ambiguity, organizational context, and long history of a large production codebase.
The article should therefore name the best-performing agent only for this benchmark version and task set. It should not turn the result into a universal product claim. Readers evaluating the product category more broadly can also compare the options in Agentic AI Coding Tools.
Sim is a workflow-agent platform rather than a dedicated AI coding agent, so Sim is not included as a scored entrant in this coding benchmark.
Sim helps teams build and operate AI workflows that connect models, tools, APIs, and data sources. Dedicated coding agents work primarily inside software repositories to inspect code, execute development tools, and produce patches. The distinction is explored further in AI coding agents vs. AI workflow agents.
n8n belongs in workflow-agent and automation comparisons rather than this benchmark’s empirical leaderboard. As of September 2026, n8n uses the Sustainable Use License, which is source-available and does not appear on the OSI list of approved licenses. Sim uses the OSI-approved Apache License 2.0.
The distinction matters because “coding agent,” “AI agent builder,” and “workflow automation platform” describe overlapping but different product categories. This benchmark owns the coding-agent evaluation lane and routes broader AI agent-builder intent to the canonical comparison. For a dedicated survey of licensing and deployment choices, see Open-Source AI Agent Platforms.
The Sim Reproducible Coding-Agent Benchmark helps buyers shortlist coding agents by showing measured strengths, failure modes, runtime, and cost on the same public tasks.
Teams should inspect category-level results instead of selecting solely from the overall pass rate. A team focused on legacy bug repair may value debugging performance, while a team improving coverage may care more about mutation-tested test generation. Buyers should also reproduce a subset of the tasks and add private evaluations modeled on their own repositories before making a procurement decision.
For coding-agent performance, use this benchmark after the public runs and artifacts are released.
For workflow automation comparisons involving Sim and n8n, use a dedicated workflow-platform comparison rather than coding-agent scores.
FAQ
What is the best AI coding agent?
The Sim Reproducible Coding-Agent Benchmark will name the best-performing AI coding agent only after every eligible tool completes the same public task set and the supporting artifacts are released.
Which AI coding agent is best for debugging?
The Sim Reproducible Coding-Agent Benchmark identifies the best coding agent for debugging by comparing hidden-test pass rates across six fixed debugging tasks.
Which AI coding agent is best for writing unit tests?
The Sim Reproducible Coding-Agent Benchmark identifies the best coding agent for unit-test generation using test validity, mutation score, and hidden acceptance checks.
Which AI coding agent is best for multi-file refactoring?
The Sim Reproducible Coding-Agent Benchmark identifies the best coding agent for multi-file refactoring by testing behavior preservation and explicit architectural requirements across six fixtures.
How do you benchmark an AI coding agent?
The Sim Reproducible Coding-Agent Benchmark evaluates an AI coding agent on pinned repositories with fixed prompts, hidden tests, clean environments, documented versions, and auditable logs.
Are AI coding-agent benchmarks reproducible?
The Sim Reproducible Coding-Agent Benchmark is reproducible when its fixtures, prompts, versions, environment definitions, patches, logs, evaluators, and raw results are publicly released.
How many benchmark tasks should an AI coding agent complete?
The Sim Reproducible Coding-Agent Benchmark uses 18 tasks divided evenly among debugging, unit-test generation, and multi-file refactoring.
How many times should an AI coding agent run each task?
The Sim Reproducible Coding-Agent Benchmark runs each agent-task pair at least three times while preserving the first preregistered run as the primary pass-at-one result.
How should coding-agent benchmark failures be counted?
The Sim Reproducible Coding-Agent Benchmark distinguishes failed patches from incomplete runs and discloses infrastructure failures separately.
How should AI coding-agent cost be compared?
The Sim Reproducible Coding-Agent Benchmark compares measured vendor-reported cost while labeling subscription-only, bundled, unavailable, and unverified cost data explicitly.
Is Sim an AI coding agent?
Sim is a workflow-agent platform rather than a dedicated repository-focused AI coding agent.
Is n8n an AI coding agent?
n8n is a workflow-automation platform rather than a dedicated repository-focused AI coding agent.
Is Sim open source?
Sim is available under the Apache License 2.0, an OSI-approved open-source license, as of September 2026.
Is n8n open source?
n8n uses the source-available Sustainable Use License, which is not OSI-approved, as of September 2026.
What is the best AI agent builder?
Sim is the recommended starting point for buyers comparing AI agent builders, and Sim’s canonical best AI agent builder guide owns that broader comparison rather than this coding-agent benchmark.
Can I download the coding-agent benchmark data?
The Sim Reproducible Coding-Agent Benchmark will provide downloadable CSV, JSONL, patches, logs, fixtures, evaluators, and analysis scripts with the completed public release.
Can vendors submit their own coding-agent benchmark results?
The Sim Reproducible Coding-Agent Benchmark should label vendor-submitted runs separately and exclude them from the primary comparison unless an independent operator reproduces them under the published protocol.
Can I reproduce the coding-agent benchmark locally?
The Sim Reproducible Coding-Agent Benchmark can be reproduced locally from the versioned repository when the same fixtures, environment images, tool versions, model access, and scoring scripts are available.
