Windsurf, launched by Codeium in November 2024 and now under Cognition AI after a December 2025 acquisition, makes a claim that separates it from every other AI code editor on the market: the AI layer is not bolted onto an existing editor as an extension. The Cascade agent system, the semantic indexing engine, and the multi-file editing orchestration were built from scratch as a unified architecture. We spent three weeks running Windsurf as our primary editor on a TypeScript monorepo and a Python data pipeline to test whether that architectural bet translates into a measurably different experience.
The Cascade Architecture: Plans Instead of Predictions
The easiest mistake when first opening Windsurf is treating Cascade like a chat window. It is not. Cascade operates as a stateful agent with a plan-then-execute loop — you give it a goal, it decomposes the goal into sequenced steps, you approve or trim the plan, and only then does it begin writing code. This is architecturally different from Cursor's tab completion model, which predicts edits reactively based on cursor position, and from Copilot's agent mode, which chains tool calls within a single prompt-response cycle.
Under the hood, Cascade runs on a two-layer architecture. A planning layer powered by SWE-1 (Codeium's proprietary model, now under Cognition AI) maps the task across the codebase by querying a semantic graph built from AST parsing — not a keyword text index. We verified this by asking Cascade to rename a utility function in our TypeScript monorepo and watching it locate all 17 references, including four that were imported through barrel re-exports. A standard text search would have missed those. Once the plan is locked, the generation layer hands individual steps to a frontier model (Claude Sonnet or GPT-4o, depending on what you select), which writes the actual code and runs terminal commands to verify the output.
The indexing pipeline is where the "built from scratch" claim carries weight. On our 8,200-file monorepo, Windsurf's initial indexing phase took 47 seconds and produced a 768-dimensional embedding per function. Subsequent lookups during editing were near-instant — we measured 180ms on average when Cascade pulled relevant context for a cross-package edit. Cursor's equivalent indexing took 23 seconds on the same repo (based on our earlier testing), but its tab completion model does not query the index with the same depth — it operates primarily on open files and cursor-adjacent context. Cascade's M-Query retrieval method, which the Codeium team built to improve precision over cosine similarity, is the reason the retrieved snippets consistently matched the actual edit target rather than surface-level keyword matches.
Multi-File Editing: What the Architecture Delivers
The feature where Cascade's plan-first design separates itself from alternatives is coordinated multi-file edits. We tested this with a refactoring task we have used as a benchmark across multiple AI editors: replacing a logging library across an Express.js API layer that touches route handlers, middleware, type definitions, and a client wrapper — 11 files total.
We gave Cascade the instruction: "Replace the winston logger with pino across the API layer. Use the async logger pattern from our existing pino configuration in config/logging.ts. Update every route handler and middleware file. Then run the test suite."
Cascade generated a seven-step plan: locate the existing pino config (step 1), identify all files importing winston (step 2), rewrite each file with the pino async pattern (steps 3-6), run tests (step 7). We trimmed step 6 because it proposed touching a client-side file that did not need the change, approved the remaining six, and watched Cascade execute. It completed in roughly 14 minutes, with two manual corrections required — one file where the import path was off by a directory level, and another where Cascade used pino.info() instead of the project's wrapped logger.info(). The test suite passed on the second run after those fixes.
For comparison, we had attempted the same refactoring in Cursor's Composer agent mode the previous month. It took three separate sessions because the context drifted after roughly 40 tool calls per session, and we had to re-establish the task constraints each time. Total time: just over two hours for the same result. The difference is not model intelligence — both tools run similar frontier models — but Cascade's explicit plan structure, which stays visible in the panel and does not degrade as the session lengthens. The model never has to reconstruct intent from conversation history because the intent is the plan.
This is not to say Cascade is universally faster. On our Python data pipeline, where we asked it to convert synchronous SQLAlchemy calls to async equivalents across eight service functions, Cascade correctly rewrote seven but silently left the eighth calling a sync session inside an async function body. The runtime error would only surface at request time, not startup. We caught it during code review. The plan structure gives you better visibility into what was done, but it does not eliminate the need to read the diff.
We found Cascade's accuracy on multi-file edits improves noticeably when you populate the Memories panel with project-specific constraints before starting. After storing rules like "use the async logger wrapper from config/, not raw pino" and "import paths use @/ aliases, not relative paths," Cascade's import-path error rate dropped from roughly one mistake per four files to one per nine files in our subsequent tests. A 30-second investment in a Memory note saved us several rounds of manual corrections.
Where Windsurf Wins and Where Cursor Still Leads
After three weeks of daily use across two languages, we can trace the differences to specific architectural decisions rather than vague impressions of "smarter" or "better."
Context awareness with large codebases. On our 8,200-file TypeScript monorepo, Cascade's semantic graph indexing meant that asking "where do we set the default user role?" returned the correct file on the first try with a citation to the exact line. Cursor's @-mention based codebase search also got there but required us to manually narrow the search scope first. When we tested on a smaller project (900 files), the difference disappeared — both tools found the right file immediately. Cascade's context advantage is real, but it only manifests above roughly 2,000 files. Below that threshold, the deeper indexing adds latency without a corresponding accuracy gain.
Speed and responsiveness. Cursor's tab completions are faster — we measured an average of 120ms latency versus Windsurf's Supercomplete at roughly 190ms. The 70ms gap is perceptible when you are typing quickly and expect instant ghost text. Cursor's edit-prediction feature, which anticipates multi-location changes as you modify a function signature, is also something Windsurf does not attempt at the tab level. For rapid iteration — writing new components, adding endpoints, prototyping — Cursor feels snappier.
Reliability on complex tasks. Cascade's plan-then-execute design means it handles long, multi-step tasks without degrading the way chat-based agents do. We ran a 14-step Cascade session that touched 19 files over 40 minutes, and the 14th step was as coherent as the 2nd. In Cursor's Composer, we consistently saw response quality decline after roughly 40 tool calls, requiring a fresh session. The tradeoff is that Cascade's planning phase adds 5-15 seconds of latency before any code appears — tolerable for a refactor, annoying for a three-line fix.
GitHub Copilot sits in a different category. Copilot's agent mode (launched in 2025) can handle multi-file edits, but it lacks the semantic indexing that both Windsurf and Cursor provide. In our logger-replacement benchmark, Copilot's agent mode found 9 of the 11 files that needed changes and missed two because they imported winston through an intermediary utility file. The generated code was clean, but the context retrieval was shallower. Copilot remains the best option if you are already in the GitHub ecosystem and do not want to switch editors — but as an agentic coding tool, it is a generation behind both Windsurf and Cursor.
Pricing and the Credit Economy
Windsurf Pro costs 15 dollars per month and includes 500 credits for Cascade and premium model usage. Supercomplete autocomplete is unlimited and does not consume credits. Cursor Pro costs 20 dollars per month with 500 premium model requests, and both tools offer pay-per-use top-ups when you exhaust the monthly allocation.
In practice, the credit systems behave differently because the tools consume credits at different rates. A single Cascade session on Windsurf — one prompt that triggers a multi-step plan — counts as one credit regardless of how many files it touches or how many tool calls Cascade makes internally. Cursor's agent mode charges per tool call, which means a complex multi-file edit can consume three to five premium requests. Over the three-week test period, we burned through 380 Windsurf credits and would have consumed roughly 450 Cursor credits for the same workload (estimated based on our prior Cursor usage patterns).
The practical difference is that Windsurf's credit model encourages batching work into fewer, larger Cascade sessions, while Cursor's per-tool-call model makes you conscious of every agent action. Neither is strictly cheaper — the math depends on whether you tend to make many small agent requests or fewer large ones. For our workflow, which leans toward periodic large refactors interspersed with tab-completion-heavy coding, Windsurf's credit economy stretched further.
Originally published at pickuma.com. Subscribe to the RSS or follow @pickuma.bsky.social for new reviews.
Top comments (0)