The coding assistant market looks crowded and behaves like a spectrum. At one end, a completion engine suggests the next line while you type. At the other, an agent takes a ticket and returns a pull request with nobody watching in between. The capability differences are real but smaller than the difference in supervision.
That framing sorts the field faster than any benchmark. This page uses Cursor, GitHub Copilot, Claude Code, Codex, and Devin as the five points on the spectrum.
The short answer
Pick the point on the spectrum that matches your review capacity. Completion needs no review; a pull request needs a reviewer; an autonomous ticket needs a ticket that can be reviewed at the end. Choosing an agent when you have no review capacity produces unreviewed code, not more capacity.
The spectrum
| Mode | Tool example | What you review |
|---|---|---|
| Inline completion | Copilot, Tabby | Nothing at write time |
| Chat in editor | Copilot, Lex | The answer before you apply it |
| Multi-file edit | Cursor, Augment | The diff |
| Terminal agent | Claude Code | Changes as they happen |
| Cloud agent | Codex, Devin | The final pull request |
The rows are ordered by when supervision happens, not by how good the output is. A tool that moves supervision later requires a review process capable of catching more at once.
Completion tools: the invisible mode
GitHub Copilot and self-hosted options like Tabby ask nothing of your process. They suggest; you accept or ignore. That is why they are the easiest to adopt and the easiest to stop noticing.
The failure mode is subtle: completion propagates existing patterns, good and bad. A repository with inconsistent error handling gets more inconsistent error handling, faster.
Editor-native assistants: the diff
Cursor changed the unit of review from a line to a diff: it edits several files and shows what changed. That is a better fit for refactors than completion, because the review is a change rather than a suggestion.
The cost is a new editor. Teams underweight that cost and then discover the friction is habits, not features.
Terminal and cloud agents: the ticket
Claude Code runs in your terminal and shows edits as they happen, which keeps a person in the loop without a separate review step. Codex and Devin take a task and return a result, which moves the loop later — to the pull request or the ticket.
That last move is the risky one: it only works when the task is describable, verifiable, and reversible. A ticket that says 'clean up the auth flow' is none of those, and an agent will produce a diff that looks complete.
What actually changes output
Three things matter more than the model: the size of the task, the state of the tests, and the quality of the review. An agent with a failing test suite writes code nobody can verify; the same agent with good tests and small tickets is genuinely useful.
The practical setup is unglamorous: test coverage first, tickets small enough to review in ten minutes, and an agent whose changes land as a diff.
A decision sequence
Start with the editor you have and add completion. If the bottleneck is multi-file edits, consider an editor-native assistant. If the bottleneck is ticket-shaped work, try a terminal agent before a cloud one — supervision is easier to keep when you can interrupt.
Move to a cloud agent last, and only for work you can describe precisely. The teams that get value from it are the ones with a review process that was already working.
Frequently asked questions
Is a cloud agent better than a terminal one? Not better — later. It trades supervision for throughput, and that trade is only good if review catches what supervision would have.
Do I need to switch editors? Only if the diff-level workflow is worth the habit cost. Many teams are better served by Copilot plus discipline.
What about self-hosting? A separate decision, driven by the data boundary. See self-hosted AI coding.
Last checked: 12 November 2026.
Related: Cursor vs GitHub Copilot, Claude Code vs Codex, and coding tools.
