Claude Code vs. Cursor: How to Compare Their Workflows (June 2025)

“Should I use Cursor or Claude Sonnet?” combines two decisions. Cursor is an editor with AI-assisted workflows. Sonnet is a model family that can be used inside products. Choosing an editor does not necessarily settle the model, and choosing a model does not settle how it sees files, runs commands, or presents a patch.

Claude Code adds another layer: a coding agent with a terminal-based workflow. A meaningful comparison needs to keep these layers separate.

This article describes the situation on 2 June 2025. Product packaging and model availability change quickly, so the aim is to compare decisions rather than preserve a price table that will soon mislead.

What changed by this date

Anthropic introduced Claude Code as a research preview alongside Claude 3.7 Sonnet in February 2025. Its 22 May Claude 4 announcement announced Opus 4 and Sonnet 4, Claude Code’s general availability, and IDE integrations for VS Code and JetBrains.

It would therefore be misleading to describe Claude Code here as a tool that can only participate in a terminal workflow with no IDE integration. The terminal remains central to its identity, but the integration options matter.

Likewise, running a local command-line interface does not mean the model’s inference happens on your computer. The Claude 4 announcement describes hosted access through Anthropic and cloud platforms; it does not announce downloadable Sonnet weights for on-premises inference.

Compare the work surface

In Cursor, the editing environment is already the place where you navigate code, inspect diagnostics, and review changes. That can make a tight cycle of selecting context, requesting an edit, and adjusting the result feel natural.

Claude Code approaches the repository through an agent that can inspect files and use development tools from a command-line workflow, with integrations available to connect that work to an editor. That can suit tasks where commands, tests, and several files are part of one investigation.

These are workflow tendencies, not exclusive capabilities or proof of superiority. The relevant question is where you can most easily understand and control the proposed change.

Decision	What to inspect
Model	The selected model and its behavior on your tasks
Context	Which files, rules, and tool results reach the model
Actions	What the agent can edit or execute, and with what controls
Review	How clearly the complete patch and test results are presented
Data handling	Where repository content is processed under your setup

Run a comparison that does not confuse the layers

Choose a small, realistic task with an acceptance condition and a known starting state. Give each workflow equivalent task context. Where possible, use the same model to examine workflow differences; in a separate experiment, keep the workflow fixed to examine models.

Record time to an accepted change, review effort, mistakes, and unresolved assumptions. Include time spent repairing the result. One successful demonstration or a tool’s published benchmark is not a measurement of your team’s productivity.

For a model from another provider, such as Gemini, the same distinction applies. A model comparison needs the exact model version and access conditions. It cannot be inferred from whether the interface happens to be an editor or a terminal.

The practical winner is the setup that helps you obtain and verify a good change in your repository. If you cannot tell which model ran, what context it received, or what it changed, the comparison has started with too many unknowns.