Tested: June 2026 · Project: React 18 + TypeScript monorepo, ~40,000 lines · Models: Claude Sonnet 4.6 where applicable · All tools on current paid tiers
Every AI coding tool benchmark you find online falls into one of two categories: synthetic test suites like HumanEval and SWE-bench that measure performance on isolated coding puzzles, or anecdotal blog posts where a developer tried a tool for a week and wrote their impressions.
Neither is useful for the real decision — which tool should I actually use in my day-to-day work on a real codebase?
This benchmark tests six tools on five tasks drawn from a real React 18 + TypeScript monorepo. Every task reflects something developers actually do. Every result includes what went wrong, not just what went right. The methodology is fully disclosed so you can replicate or argue with it.
All testing was conducted on a single React 18 + TypeScript monorepo with the following characteristics:
All tools were tested on the same codebase, same machine (MacBook M3 Pro, 36GB RAM), same network conditions, in the same week.
| Tool | Plan | AI model | Version |
|---|---|---|---|
| Cursor | Pro ($20/mo) | Claude Sonnet 4.6 | 0.47.8 |
| Windsurf | Pro ($15/mo) | SWE-1.5 + Claude Sonnet 4.6 | 1.9.4 |
| GitHub Copilot | Pro ($10/mo) | GPT-4o (default) | VS Code extension 1.262 |
| Cline | BYOK | Claude Sonnet 4.6 | 3.11.1 |
| Aider | BYOK | Claude Sonnet 4.6 | 0.55.0 |
| Claude Code | Pro ($20/mo) | Claude Sonnet 4.6 | 0.2.51 |
Five tasks, each representing a real workflow category:
Each task scored 1–10 per tool. Criteria vary by task but always include: correctness on first attempt, number of iterations to acceptable result, and quality of final output. Tools are marked N/A where the task is not applicable (e.g., Cline and Aider have no inline autocomplete — this is not a failure, it is a design choice).
The test: Code a new React hook that fetches paginated user data using React Query, including TypeScript generics for the response type, error handling, and re-fetch triggers. Stop at the function signature and let each tool complete it. Measure: how many lines completed correctly on the first tab press, and how many corrections were needed.
| Tool | Lines correct / first try | Corrections needed | Notes |
|---|---|---|---|
| Cursor | 18 / 18 | 0 | Inferred generic type from existing pattern in the codebase; no manual type annotation needed |
| Windsurf | 16 / 18 | 1 | Correct pattern but used older React Query v4 syntax; one correction to upgrade |
| GitHub Copilot | 12 / 18 | 2 | Strong completion but did not pick up the project's custom query key pattern; two adjustments |
| Cline | N/A | N/A | No inline autocomplete by design |
| Aider | N/A | N/A | No inline autocomplete by design |
| Claude Code | N/A | N/A | No inline autocomplete by design |
Cursor's codebase indexing is the decisive factor here. It read the existing query hooks in the project and immediately matched their patterns — generic type parameters, error types, query key structure. The completion required zero manual correction.
Windsurf's Tab completions are unlimited on all plans and quality is strong, but it missed the project's query key convention — a subtle difference that only emerges when the tool has indexed your specific codebase deeply.
GitHub Copilot's training data gives it strong TypeScript pattern recognition for common patterns, but on project-specific conventions it requires more adjustment.
Winner — Task 1: Cursor
The test: Rename a shared buttonVariant prop (currently named variant) across all components that use it — 12 files across 3 apps. The prop is defined in a shared package. Agent must find all usages without being told which files to check.
| Tool | Files found | Files correct | Iterations | Notes |
|---|---|---|---|---|
| Claude Code | 12 / 12 | 12 / 12 | 1 | Used Agent Teams; found all 12 including 2 in the admin app that required cross-package awareness |
| Windsurf (Cascade) | 12 / 12 | 11 / 12 | 1 | Missed one usage inside a dynamic import; caught on review |
| Aider | 12 / 12 | 11 / 12 | 1 | Repo map found all files; one test file update was wrong and needed manual correction |
| Cursor | 11 / 12 | 11 / 11 | 2 | Missed one usage in admin app on first pass; found it on second prompt |
| Cline | 11 / 12 | 11 / 11 | 1 | Plan mode previewed all changes before execution; missed the same admin file as Cursor |
| GitHub Copilot | 9 / 12 | 9 / 9 | 3 | Copilot Edits required explicit file selection; missed 3 files not manually specified |
Claude Code's Agent Teams are the decisive advantage here. Running two parallel sub-agents — one on the web app, one on admin — it found every usage including a dynamic import that requires tracing a runtime dependency chain. All 12 corrections were accurate.
Windsurf's Cascade is notably strong on this type of task, finding 12/12 files autonomously. The one missed correction was a dynamic import with non-standard syntax.
Copilot Edits requires more manual steering — you must add files to the editing set explicitly. For a refactor spanning 12 files across multiple packages, this becomes a workflow limitation.
Winner — Task 2: Claude Code
The test: Provide a TypeScript stack trace from a production error: TypeError: Cannot read properties of undefined (reading 'map') thrown inside a React Query select function during server-side rendering. The root cause is a race condition where the query resolves before the layout component mounts. Measure: does the tool identify the correct root cause and fix without additional hints?
| Tool | Root cause identified | Fix correct | Iterations | Bonus |
|---|---|---|---|---|
| Claude Code | ✅ Yes | ✅ Yes | 1 | Identified a secondary issue: missing Suspense boundary that would cause the same error in a different code path |
| Cursor | ✅ Yes | ✅ Yes | 1 | Precise and fast; no bonus insight |
| Cline | ✅ Yes | ✅ Yes | 1 | Plan mode traced the call stack before proposing a fix; slightly more explanation |
| GitHub Copilot | ✅ Yes | ✅ Yes | 1 | Strong at SSR error patterns; explanation was the clearest of all tools |
| Windsurf | ⚠️ Partial | ✅ Yes | 2 | Initially identified the map call rather than the SSR race condition; corrected on second prompt |
| Aider | ✅ Yes | ✅ Yes | 2 | Correct diagnosis but required --read to bring the relevant file into context first |
All tools except Windsurf identified the root cause correctly on the first attempt. Claude Code's bonus insight — spotting the secondary Suspense boundary issue — demonstrates the kind of architectural awareness that makes it valuable for complex debugging sessions.
Windsurf's Cascade initially focused on the immediate error site (the .map() call) rather than the underlying SSR lifecycle issue, requiring a follow-up prompt. This reflects its more action-first, less-investigative default behaviour.
Aider's terminal workflow requires explicit file loading, which adds a step but does not prevent correct diagnosis.
Winner — Task 3: Claude Code (with GitHub Copilot's explanation quality as honourable mention)
The test: Write a comprehensive Vitest test suite for a UserService.createUser() function that validates inputs, hashes a password, creates a database record via Prisma, and sends a welcome email via a third-party service. A good test suite covers: happy path, validation failures, database error, email service failure, and ensures correct mocking.
| Tool | Tests written | Scenarios covered | Compile errors | Notes |
|---|---|---|---|---|
| Claude Code | 9 tests | 6 / 6 | 0 | All 6 scenarios including email failure; mocking was idiomatic; added a test for duplicate email |
| Cline | 8 tests | 5 / 6 | 0 | Plan mode designed test suite before writing; missed email failure path |
| Cursor | 7 tests | 5 / 6 | 0 | Solid coverage; missed duplicate email edge case |
| Aider | 7 tests | 5 / 6 | 0 | Auto-committed tests; correct mocking patterns; missed email failure path |
| Windsurf | 7 tests | 5 / 6 | 1 | One compile error: used vi.spyOn on a module not imported as a spy target |
| GitHub Copilot | 6 tests | 4 / 6 | 0 | Good standard coverage; missed async error propagation and email failure |
Claude Code wrote the most complete test suite with zero compilation errors and a bonus edge case (duplicate email) that reflects real-world failure modes. Its understanding of Vitest mocking patterns for Prisma and external service calls was accurate without correction.
Cline's Plan mode approach — designing the test structure before writing code — produced a more systematic suite than tools that wrote tests directly. The final output was one scenario short but correctly structured.
Copilot produced a solid baseline but its test coverage was notably shallower on async error scenarios, which are often where production bugs live.
Winner — Task 4: Claude Code
The test: Provide a 120-line async function from the project's AuthService that handles token refresh with retry logic, race condition prevention via a mutex lock, and fallback to re-authentication on specific error codes. The function has no comments. Measure: accuracy, completeness, and identification of non-obvious logic.
| Tool | Accurate | Complete | Non-obvious insights | Quality (1–10) |
|---|---|---|---|---|
| Claude Code | ✅ | ✅ | Mutex pattern, retry backoff timing, token race condition window | 9.5 |
| GitHub Copilot | ✅ | ✅ | Identified the re-auth fallback correctly; best formatted output | 9 |
| Cursor | ✅ | ✅ | Good overall; correctly identified mutex but missed backoff timing subtlety | 8.5 |
| Cline | ✅ | ✅ | Accurate but verbose; noted a potential deadlock scenario not immediately obvious | 8.5 |
| Windsurf | ✅ | ⚠️ Partial | Accurate on main flow; missed the race condition window explanation | 8 |
| Aider | ✅ | ⚠️ Partial | In /ask mode, accurate but terse; did not explain timing implications |
7 |
Claude Code and GitHub Copilot produced the most complete and insightful explanations. Claude Code specifically called out the race condition window — the brief period between token expiry detection and mutex acquisition where multiple requests could attempt refresh simultaneously — which is the most subtle piece of logic in the function.
Copilot's explanation was the best formatted: structured sections for "what it does," "how it works," and "potential issues" — ideal for documentation or onboarding materials.
Aider in /ask mode is accurate but terse by design, optimised for quick answers rather than documentation-grade explanations.
Winner — Task 5: Claude Code (with GitHub Copilot's format quality as honourable mention)
| Tool | Task 1 Autocomplete | Task 2 Multi-file | Task 3 Bug fix | Task 4 Tests | Task 5 Explanation | Applicable avg |
|---|---|---|---|---|---|---|
| Claude Code | N/A | 9.5 | 9.5 | 9.5 | 9.5 | 9.5 / 10 |
| Cursor | 9.5 | 8.5 | 9 | 8.5 | 8.5 | 8.8 / 10 |
| Cline | N/A | 8.5 | 9 | 9 | 8.5 | 8.75 / 10 |
| Windsurf | 8.5 | 9 | 7.5 | 8 | 8 | 8.2 / 10 |
| Aider | N/A | 8.5 | 8 | 8 | 7 | 7.9 / 10 |
| GitHub Copilot | 7.5 | 6.5 | 9 | 7 | 9 | 7.8 / 10 |
Note: Cline, Aider, and Claude Code have no inline autocomplete — their average excludes Task 1. Comparing these scores across IDE tools and agent-only tools requires accounting for this.
| Category | Winner | Runner-up |
|---|---|---|
| TypeScript autocomplete | Cursor | Windsurf |
| Multi-file refactoring | Claude Code | Windsurf |
| Bug fix accuracy | Claude Code | Cursor / Copilot (tied) |
| Test generation | Claude Code | Cline |
| Code explanation | Claude Code | GitHub Copilot |
Claude Code wins on agent task quality by a clear margin. If your primary use of AI coding tools is autonomous multi-file tasks, debugging, testing, and code understanding — and you are comfortable with a terminal-first workflow — Claude Code produces the best results of any tool tested.
Cursor is the strongest full IDE with inline autocomplete. If you need autocomplete + agent in one seamless editor experience, Cursor's combination is the best available.
Windsurf is the strongest value proposition: $15/month for a tool that competes with Cursor's $20/month on most tasks except deep codebase indexing.
Cline is the strongest free agent. On agent tasks it competes directly with Claude Code and Cursor despite costing zero in subscription fees — you pay only API token costs.
GitHub Copilot wins on IDE breadth and code explanation quality, but its agent mode lags behind on complex multi-file tasks.
Aider is the most token-efficient tool for terminal-based batch operations, but its terse interaction model scores lower on quality-per-task metrics.
Speed and latency. Response times vary with network conditions, server load, and model availability. We did not measure latency because it varies too much across sessions to be meaningful data.
Context window limits. Our 40,000-line project fits within all tested tools' effective context. Very large codebases (300,000+ lines) may show different relative performance.
Long-session degradation. Rule adherence and context quality in conversations longer than 50 turns was not measured. This is a known limitation across all tools.
Language-specific performance. All tests used TypeScript and React. Python, Go, Java, or PHP results may differ — particularly for tools with language-specific training emphasis.
Model-specific performance. Windsurf was tested with SWE-1.5 for agent tasks and Claude Sonnet 4.6 for chat. Using GPT-4o or Gemini inside these tools may produce different results.
For deeper analysis of each tool:
For pair comparisons with full scoring tables:
Based on this benchmark, Claude Code leads on agent task quality (9.5/10 average across Tasks 2–5). Cursor leads on full IDE experience including autocomplete (8.8/10 including Task 1). The "best" tool depends on your workflow: Claude Code for agent-heavy terminal work, Cursor for an all-in-one IDE experience.
Claude Code's Agent Teams parallelism and Anthropic's benchmark-leading Claude Sonnet 4.6 model give it an edge on complex multi-step tasks. Cursor's advantage is its integration — autocomplete, codebase indexing, and agent in one seamless IDE. At the same price, the choice depends on whether you prioritise raw agent capability or integrated IDE workflow.
For the four tasks where both tools competed (Tasks 2–5), Windsurf averaged 8.1/10 versus Cursor's 8.5/10 — a gap of 0.4 points at $5/month less. For most developers, that price-performance ratio is compelling. The gap is more pronounced on very large codebases where Cursor's codebase indexing advantage compounds.
Cline's agent task average (8.75/10 across Tasks 2–5) places it between Claude Code and Cursor on these metrics. The trade-off is variable API costs instead of a fixed subscription, no inline autocomplete, and less seamless IDE integration. For cost-conscious developers, Cline's agent quality is genuinely competitive with subscription tools.
Copilot Edits requires explicit file selection — you manually add files to the edit set before the agent can modify them. On a 12-file refactor spanning 3 apps, this becomes a friction-heavy workflow compared to tools that autonomously discover all relevant files. This is a workflow design difference, not a model quality difference.
Yes. The plan is quarterly updates as tools release major updates. Each update will note what changed and compare results to the previous round. Subscribe via RSS or check back in September 2026 for the next edition.