Goatfied vs Cursor vs GitHub Copilot: how to benchmark them on real PR tasks

Most "AI coding assistant" comparisons you'll find online rank tools by how good a demo looks. That tells you almost nothing about what happens when the same tool touches a 200k-line repository with CI gates, a reviewer who's seen every trick, and a merge queue that punishes flaky changes. This guide gives you a benchmark you can actually run yourself — the tasks, the metrics, and the harness — so you can compare Goatfied, Cursor, and GitHub Copilot on your own code instead of trusting anyone's marketing.

We're not going to hand you a leaderboard with fabricated win rates. Numbers only mean something on your repo, with your test suite. What we can give you is a rigorous method.

Why demo benchmarks mislead

Greenfield demos reward the wrong things. A tool that scaffolds a to-do app in one shot may still fall apart the moment it has to:

• respect an existing architecture instead of inventing one,
• make a change that spans five files without breaking a sixth,
• pass a compile step, a linter, and a targeted test before a human ever looks at it.

Production work is dominated by _constraints_, and constraints are exactly what a slick demo hides. So the benchmark has to be built from real pull-request tasks, scored by the same gates your team already trusts.

The task set: 50 real PR-shaped tasks

Pick 50 tasks that look like your actual backlog. A good distribution:

• **20 bug fixes** with a failing test that must go green.
• **15 small features** with an acceptance checklist written _before_ prompting.
• **10 multi-file refactors** (rename a core type, extract a module, change a signature used in a dozen call sites).
• **5 "trap" tasks** that look simple but have a hidden constraint (a public API you must not change, a migration that must stay backward-compatible).

Freeze the starting commit for every task so all three tools see identical inputs. Record the branch, the full transcript, and every command each tool ran. If you can't trace _why_ a change passed or failed, the result isn't evidence — it's an anecdote.

The metrics that actually predict delivery quality

Speed is the metric everyone quotes and the one that matters least. These are the four we weight highest:

1. **Compile-first rate** — share of tasks where the first produced change set compiles and passes lint with no human edits. This is the single strongest proxy for "did the tool understand the code."

2. **Test-pass rate** — of the changes that compile, how many pass the targeted tests you defined up front.

3. **Review churn** — number of review cycles and lines the reviewer had to rewrite before merge. Low churn is where real time is saved.

4. **Two-week regression count** — reopened issues and post-merge regression tickets in the fortnight after merge. This catches tools that trade short-term speed for long-term rework.

Track cycle time too, but as a tiebreaker, not the headline.

The harness

Keep it boringly deterministic:

# For each tool T and task task_id:

git checkout $BASE_COMMIT -B bench/$T/$task_id

run_tool $T --task tasks/$task_id.md --repo . \

  --log runs/$T/$task_id.log

# Objective gates — identical for every tool:

make compile            # gate 1: does it build?

make lint               # gate 2: style/static analysis

make test TASK=$task_id # gate 3: targeted tests

Every tool gets the same three gates and the same reviewer rubric. Reviewer sign-off criteria are written down _before_ anyone sees model output, which keeps scoring honest.

How to read the results

A few patterns show up consistently once you run this on real code, and they explain Goatfied's design choices:

• **Constraint discipline beats raw model quality.** The tools that let you attach explicit compile and test gates to the task produce fewer dead-end runs. This is why Goatfied's agent loop treats "compile, lint, targeted test" as a required gate rather than an optional afterthought — the loop won't hand you a change it hasn't validated.
• **Small, reversible diffs win on churn.** Broad speculative rewrites look impressive and cost you in review. Goatfied is built to keep change sets small enough to roll back in minutes, which is what actually earns reviewer trust in autonomous execution.
• **Precise context beats big context.** Assistants that vacuum up half the repo tend to hallucinate architecture. Retrieval scoped tightly around the changed files consistently outperforms.

Running it fairly

• Use identical starting commits and identical gates for all three tools.
• Don't hand-fix a tool's output and then count it as a pass — that's measuring you, not the tool.
• Give each tool the same task description, phrased the way your team really writes tickets.
• Run each task at least twice; note variance. A tool that's great once and useless the next run is not production-ready.

Where Goatfied fits

Goatfied is an AI-native editor and cloud platform built for reproducible automation, compile-first reliability, and audit-friendly workflows. On a benchmark like this it's optimized to do well on the metrics that predict delivery quality — compile-first rate, review churn, and regressions — rather than the ones that make for a good screen recording. That's a deliberate trade: we'd rather lose a speed demo than win a merge that reopens a week later.

Run the benchmark on your own repository. If a tool can't clear your compile, lint, and test gates on your real backlog, no leaderboard should convince you otherwise.

Related reading

• [The Goatfied agent loop: how we ship code that compiles first try](/blog/goatfied-agent-loop-compiles-first-try)
• [Multi-file refactors with Goatfied: case studies from real codebases](/blog/multi-file-refactors-with-goatfied-case-studies)