Cursor vs Claude Code vs Codex: which handles context best?

If you've used more than one AI coding agent for more than a week, you've noticed they don't behave the same. Cursor feels intuitive but loses the thread on big refactors. Claude Code is patient and methodical but expensive. Codex is fast but stubborn.

A lot of those differences come down to one thing: how each tool handles context. Here's a practical comparison, and what it means for how you set them up.

Cursor

Strengths. Cursor lives inside your editor and indexes your repo on the fly. Tab completions and inline edits feel native. The @ mentions for files, docs, and web sources are still the cleanest UX in the category.

Weaknesses. In our use, Cursor weights what's currently open more than the rest of the repo. Files you haven't touched recently fall out of relevance, and on large codebases it samples aggressively — which means it'll confidently rewrite a function while ignoring the helper you wrote two months ago that does the same thing.

Best with: a .cursorrules file at the repo root that names the patterns you care about, plus aggressive use of @ to pin context for any non-trivial change.

Claude Code

Strengths. Claude Code reads more, plans more, and corrects itself more than the other two. Long context windows mean it can hold an entire feature in working memory. It will read your CLAUDE.md on every session, which is the closest thing any of these tools has to durable memory.

Weaknesses. It is the most expensive of the three, and the planning step that makes it good also makes it slow on small tasks. For "rename this variable" it is overkill. It also still forgets between sessions — the only persistent thing is the file you give it to read.

Best with: a comprehensive CLAUDE.md and a .claude/agents/ directory with named subagents for the workflows you repeat.

Codex

Strengths. Codex (the OpenAI agent, not the deprecated 2021 model) is fast and tightly integrated with GitHub. It runs autonomously well — give it a task and walk away. Strong at structured changes across many files.

Weaknesses. In our use, Codex defers least to existing conventions. It has its own ideas about how things should be done, and without strong guardrails it'll generate clean, idiomatic code that doesn't match anything around it.

Best with: an AGENTS.md file enumerating the patterns it must respect, plus narrow task scopes — Codex shines on focused jobs, drifts on open-ended ones.

At a glance

The thing they all have in common

Each of these tools has its own native way to give it persistent context: .cursorrules, CLAUDE.md, AGENTS.md. They are similar enough that most teams write the same content into all three files and copy-paste between them.

That's the real comparison. None of them remember anything between sessions on their own. All of them rely on you to feed them the same context, every time, in slightly different formats.

The answer to "which handles context best?" is the unsatisfying one: whichever one you give the most context to, in the format it expects.

A practical setup

If you're using more than one of these tools — and most teams now are — you have a few ways to handle the duplication:

• Maintain three context files in parallel. Write .cursorrules, CLAUDE.md, and AGENTS.md separately. Update each when conventions change. Accept the drift.
• Symlink to a shared file. Cheap, fragile, but works. Each agent reads the same source.
• Use a memory layer. Write briefs once, generate the agent-specific files from one source, query them from the CLI. That's what Brifly is.

Pick whichever you'll actually maintain. The drift is the enemy, not the tool.

The takeaway

There's no winner in this comparison. The tools have real differences in how they retrieve and weight context, but none of them solves the fundamental problem: agents don't remember, you do.

Pick the agent your team likes. Then build the context layer that feeds all of them.