benchr
Cursor, GitHub Copilot, Windsurf, and Sourcegraph Cody expose different combinations of editor integration, agent controls, model access, and repository context. Official product pages can document those surfaces. They cannot establish which complete workflow will perform best in your codebase, and forum anecdotes are not a controlled comparison.
Use public model benchmarks such as SWE-bench only as background evidence about a model under a particular harness. An editor agent adds retrieval, prompting, tool use, permissions, and diff application, so the end-to-end result must be measured separately. The worksheet below turns those layers into checks you can repeat.
How to read this comparison
The factual baseline is each product's official page: Cursor, GitHub Copilot, Windsurf, and Sourcegraph Cody. SWE-bench Verified is useful context for model performance under its own harness, but it does not score editor integration or your repository workflow.
For every tool, distinguish a documented feature from a hypothesis to test. Record the product version, selected model, permissions, and task inputs. Run each candidate in a clean branch and judge only the final diff, tests, latency, and reviewer effort.
Cursor: evaluation notes
Cursor presents an AI-focused editor with agent features and a model catalog. Verify the models and plan limits available to your account on the day of testing; do not infer end-to-end editor quality from a model-only benchmark.
Test whether the current build exposes a useful plan or preview before edits, whether permission boundaries are clear, and whether the proposed file scope matches the eventual diff. Record any files changed outside the request.
Use at least one localized fix, one multi-file refactor, and one test-repair task. Score compile and test results, conformity with repository patterns, unrelated edits, and minutes of human correction.
Windsurf (Cascade agent)
Windsurf's product page centers the Cascade agent. Confirm the current model controls, plan limits, and data settings in the tested account because those details can change independently of the article snapshot.
Test when the product presents a plan, asks for approval, applies edits, and exposes tool logs. Compare time to first correct patch and time to a reviewed, test-passing patch; speed without review cost is an incomplete measure.
Run the identical task pack used for Cursor. Keep model access and context as comparable as the products allow, and record any difference you cannot control as a limitation rather than attributing it to the editor.
GitHub Copilot in VS Code
GitHub documents Copilot features across supported IDEs. Existing IDE fit can reduce migration cost, but that is a team-specific benefit. Evaluate inline completion and agent work as separate products because they have different tasks and pass criteria.
For completion, measure accepted suggestions and later reverts over a representative work session. For the agent, use the same repository tasks as the other tools and measure test-pass rate, intervention count, unrelated edits, and review time. Record the selected model or routing setting when the product exposes it.
Check the current plan page, usage limits, enterprise controls, and supported IDEs before calculating value. The AI agents piece provides a broader checklist for bounded agent workflows.
Sourcegraph Cody
Sourcegraph positions code search and codebase context as a Cody differentiator. Treat that as a feature claim to verify. Ask architecture questions with known answers and measure whether the tool retrieves the right files and cites the relevant code before testing generation.
Keep retrieval and generation scores separate. A correct file list does not prove that a generated patch compiles or follows local patterns. Use the same implementation tasks as the other candidates and score both phases independently.
If retrieval is the primary need, weight file recall and citation precision more heavily. If autonomous editing is the need, weight tests and review effort. Check the current product page for supported editors, models, limits, and price before subscribing.
The headline benchmarks hide what actually separates these tools: the texture of the code each one leaves behind for you to live with.
How to run the shootout
Create a held-out task pack from real repository work: a localized bug, a multi-file feature, a refactor with invariants, a failing-test repair, and a codebase question with a known answer. Reset to the same commit before every run. Keep prompts fixed, use comparable models where possible, and save the full transcript and diff.
For a team trial, add security review, SSO and audit requirements, data-retention settings, administration time, usage limits, and migration cost. Use the current commercial quote rather than an article's historical list price. A lower seat price can still cost more if review time rises; calculate both.
Correctness
Run testsCompile, unit, integrationScope
Inspect diffUnrequested files and changesContext
Known answersFile recall and citationsIntervention
Count turnsRedirects and retriesReview
Time itMinutes to approved patchCost
Use invoiceSeat, usage, admin, reworkCursor
Test Current model catalog and agent controlsCursor evidence
Record Plan, diff, tests, review timeWindsurf
Test Cascade controls and current modelsWindsurf evidence
Record Plan, diff, tests, review timeCopilot
Test Completion and agent separatelyCopilot evidence
Record Acceptance, diff, tests, review timeCody
Test Retrieval and generation separatelyCody evidence
Record Recall, citations, diff, testsUse held-out tasks and the same repository commit for every candidate.
Product version, plan, model, permissions, context, and prompt.
Tests, unintended changes, interventions, latency, and review time.
None of these tools replaces a careful human reader. Read the diff.
What this comparison does not measure
Three things a short trial may not capture. First: total team cost. Include current seat and usage charges, administration, security review, migration, and human rework. The price-per-use-case piece explains the broader cost model.
Second: editor preference. Existing habits, extensions, accessibility settings, and debugging workflows create real switching cost. Measure onboarding time instead of assuming it is negligible.
Third: product change. Model catalogs, limits, and agent controls move quickly. Record the tested date and configuration and schedule a re-evaluation after material changes. The prompt-engineering piece covers how to version the prompts used in that evaluation.
The decision that holds up
Choose the tool that passes your repository tasks with the lowest acceptable combination of review effort, unintended change, security risk, and total cost. Different teams can reach different defensible choices because their IDE constraints and task mix differ.
Whichever tool you pick, read every diff and run the repository's checks. Track escaped defects and review time after deployment so the initial trial can be challenged by production evidence. Treat any agent as an untrusted code contributor with bounded permissions.