How do you compare Copilot, Cursor and Claude Code ROI?

Compare them on the same outcomes, for the same kind of work, over the same window, and set each vendor's own usage dashboard aside. Tag every merged pull request with the tool that produced it, then read cycle time, review time, rework and cost per merged PR by tool. The tool with the better return is the one whose PRs merge cleanly at the lower blended cost. Most teams now pay for more than one of these tools, so whether the AI coding investment is paying off often comes down to which one earns its budget. Rerun the comparison each quarter, since AI in software development moves fast enough that last year's leader can slip.

Each dashboard counts its own activity: suggestions accepted, chats opened, agent sessions run. Those counts aren't comparable across tools, and none of them follows the code to a merged pull request. Return lives downstream, in what merged and what it cost, so the comparison has to run on data every tool touches: your git history.

An accepted suggestion and a finished agent task are different units. A team can accept thousands of short completions in a week and merge the same number of PRs it did before. Another can run a handful of agent sessions that each open a PR, some of which never merge. Put both on one chart and the chart measures the tools' telemetry, not their value.

An assistant speeds up a developer inside a change that developer owns, so its return shows up in that developer's cycle time and review load. An agent produces a change on its own, so its return is whether the PR merges and how much human review it needed.

That line has moved fast.

"2025 was the year of agents with Claude Code and Codex and Cursor and of course, um, Copilot agent."
— Thomas Dohmke, CEO at Entire and former CEO of GitHub, on Dev Interrupted, Stop pretending you read every line of code

So a comparison by product name is weaker than a comparison by mode. Inline completion, chat in the editor and an agent running a task end to end are three different cost shapes, and one product can offer more than one of them.

On outcomes, measured the same way for every tool: merged PRs, cycle time, review time, rework and blended cost per merged PR. What differs is the mode each tool is used in and where its cost shows up. Read the table by mode first, then by product, because the same product can sit in two columns.

Check the plans you actually bought before you assign a tool to a column, since pricing and packaging change often.

Pick teams doing comparable work, take a baseline window before any tool change, and tag each merged PR with the tool or agent behind it from commit and PR data, not self-reports. Then read the same outcomes per tool at team level, and add blended cost last. Five steps, in order:

1. Choose comparable teams. Similar codebases, similar work mix, similar baseline cycle time.
2. Fix the windows. A baseline before the change and a read of the same length after it.
3. Tag merged PRs by tool from commit and PR metadata. Self-reported tags drift within weeks.
4. Read the outcomes per tool: merged PRs, cycle time, review time and rework, each against the team's own baseline.
5. Add cost: seat cost plus metered usage for each tool, blended with people cost, for cost per merged PR. The attribution method behind that step is part of the same tree.

Your own history is one baseline. LinearB benchmarks AI-assisted and agentic PRs against 8.1 million pull requests from 4,800 teams in 42 countries, so a tool's numbers can be read against more than your own trailing months.

That question has its own page, is GitHub Copilot worth it?, which covers Copilot's own evidence and rollout. This page takes a different job: putting Copilot beside Cursor and Claude Code on one set of measures, so the decision is a comparison rather than a verdict on one tool in isolation.

When the teams chose their own tools. Early adopters pick the new agent and also tend to be the strongest engineers, so the agent looks better than it is. Read each tool on teams with similar baselines, and treat a gap smaller than those teams' normal month-to-month swing as no gap at all.

Agent spend has a failure mode assistant spend mostly doesn't: work nobody assigned. Dohmke puts it in headcount terms.

"I think some of these companies have the problem that effectively they hired 1,000 interns."
— Thomas Dohmke, CEO at Entire and former CEO of GitHub, on Dev Interrupted, Stop pretending you read every line of code (read the recap)

His fix is the one startups use for people: a few main quests, a small allowance for side quests.

"you cannot make the side quests that the a- agents are following, uh, all of a sudden 90% of your, uh, uh, token budget."
— Thomas Dohmke, CEO at Entire and former CEO of GitHub, on Dev Interrupted, Stop pretending you read every line of code

For a tool comparison, that means reading agent cost per merged PR, not cost per session. An agent that runs cheaply but merges little is the expensive one.

Should we standardize on one AI coding tool?

Not on return alone. If two tools land within the teams' normal variation, the deciding factors are security review, data handling and what your developers will keep using. Standardize when one tool's merged-PR outcomes beat the other's on comparable teams by more than that variation.

How long should a tool comparison run?

Long enough to cover at least one full release cycle on both sides of the change, with the baseline and the read the same length. Shorter reads mostly measure the novelty bump.

Does adoption tell us which tool is winning?

No. Adoption says a tool is being used. Merged PRs and cost per merged PR say whether it pays. Measure adoption to find where a tool hasn't landed yet, and judge return on outcomes. The parent page explains why adoption and token spend can't answer the return question.