Cursor vs Windsurf Benchmarks 2026: Real Numbers
Both score ~77% on SWE-Bench Verified using their built-in agentic harnesses. The differences are in raw speed and per-model trade-offs.
Updated 17 April 2026
# Benchmark Comparison Table
| Tool / Model | SWE-Bench Verified | CursorBench | Tok/s | Context |
|---|---|---|---|---|
| Cursor (harness)(Composer 2) | ~77% | 61.3 | 200+ | 128k |
| Windsurf (harness)(SWE-1.5) | ~77% | N/A | 950 | 32k |
| Claude Opus 4.6(Anthropic) | 80.8% | N/A | ~100 | 200k |
| Claude Sonnet 4.6(Anthropic) | ~70% | N/A | ~150 | 200k |
| GPT-5(OpenAI) | ~75% | N/A | ~120 | 128k |
| Gemini 3.1 Pro(Google) | ~72% | N/A | ~140 | 200k |
Harness scores reflect the tool's agentic harness using the specified model with iterative correction. Model-alone scores are lower. CursorBench is Cursor's proprietary evaluation set.
# SWE-Bench Verified Scores (Visual)
# Why Benchmarks Lie (and What to Actually Trust)
The most important thing to understand about SWE-Bench scores is the difference between a model score and a harness score.
Model score (e.g. SWE-1.5 = 40%)
The raw model given a problem and one attempt. No iteration, no tool use, no error correction. This is the fair comparison between models in isolation.
Harness score (Windsurf = ~77%)
The full tool, with agent loop, terminal access, error reading, and multiple iterations. SWE-1.5 achieves ~77% because it can attempt a problem many times at 950 tok/s.
Other concerns: training data leakage (models may have seen SWE-Bench problems), cherry-picked evaluation sets, and the gap between benchmark tasks and real developer workflows. CursorBench (Cursor's own eval) is not independently verified.
# Speed Deep-Dive: 950 tok/s vs 200+ tok/s
Windsurf SWE-1.5: 950 tok/s
Running on Cerebras hardware, SWE-1.5 generates at roughly 950 tokens per second. This is 4-5x faster than typical cloud model speeds.
At 950 tok/s: a 500-token response takes ~0.5 seconds. An agent loop with 10 iterations of 500 tokens each completes in ~5 seconds total generation time.
Cursor Composer 2: 200+ tok/s
Composer 2 runs at 200+ tok/s. Slower than SWE-1.5, but Cursor prioritises diff quality and review-before-commit over raw iteration speed.
At 200 tok/s: a 500-token response takes ~2.5 seconds. The same 10-iteration loop takes ~25 seconds. Real-world latency depends on network and API queue time.
What this means for you
For autonomous agent loops where the model runs 50-200 iterations on a complex task, Windsurf's 4-5x speed advantage is significant. For single-shot diff generation where you review every change, the speed difference matters less than output quality. Both feel fast for interactive use.
# Accuracy: The Speed-Accuracy Trade-Off
SWE-1.5 achieves ~40% accuracy per attempt but 950 tok/s. Claude Opus 4.6 achieves ~80% accuracy per attempt but ~100 tok/s. For iterative agent work, the math often favours speed.
| Model | Single-attempt accuracy | Speed | Best for |
|---|---|---|---|
| SWE-1.5 | 40% | 950 tok/s | Many fast iterations |
| Claude Opus 4.6 | 80.8% | ~100 tok/s | Single careful attempt |
| Claude Sonnet 4.6 | ~70% | ~150 tok/s | Balanced daily use |
| GPT-5 | ~75% | ~120 tok/s | Broad task variety |
| Composer 2 | ~61% | 200+ tok/s | IDE diff workflow |
# Real-World Task Results
| Task | Cursor | Windsurf | Edge |
|---|---|---|---|
| Refactor: rename + extract function | Excellent diff preview | Excellent, faster | Tie |
| Bugfix: logic error in API handler | Good single-pass | Agent loop catches edge cases | Windsurf |
| New feature: POST endpoint + tests | Clear diff-review flow | Autonomous, multi-file | Tie |
| Test writing: unit tests for service | Good template adherence | Slightly better coverage | Windsurf |
| Monorepo nav: cross-package refactor | @codebase indexing helps | Needs more context priming | Cursor |
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