Cursor vs Codex: An Application‑Level Comparison and Insights

Test Setup

The author prepared two empty directories on Windows 11, one for Cursor and one for Codex, and gave each the same tasks using the models Cursor Composer 2.5 Fast and Codex GPT‑5.5 (high‑reasoning). No formal benchmark was performed; the focus was on practical execution.

Simple File Tasks

Both agents were first asked to report the current working directory and then create a file a.txt. The goal was to verify whether they truly understood the filesystem context and performed the operation rather than merely replying with a confirmation.

A slightly more complex task required creating a three‑level directory structure ( a/b/c) with a readme file in each level containing the 1‑9 multiplication table. This test exposed each agent's execution habit: whether it manipulates files directly, explains first, completes the work in one step, or needs iterative prompts.

Requirement Analysis: WeChat Lottery Mini‑Program

“A listed company wants a WeChat lottery mini‑program for 2,000 stores. Orders over 399 ¥ get one draw; each order can draw only once. Each store has a unique QR code and independent prize inventory. Design an MVP, list the features, no code.”

Cursor produced a PRD‑style output, breaking down background, roles, user side, store side, headquarters backend, core business rules, data integration, and MVP closure, and even prioritized features (P0, P1, P2). The author notes the format feels very structured and ready for review, though slightly formal.

Codex delivered a narrative explanation of the workflow: headquarters creates the activity, imports stores, generates QR codes, users scan, place orders, draw, and stores verify prizes. The description is more readable, though less tabular.

Database Table Design

Both agents were asked to generate MySQL 8.0 schema files for the MVP.

Cursor’s file layout:

test_cursor/
├── docs/
│   └── lottery-mvp.md
└── database/
    └── mysql8/
        ├── README.md
        └── schema.sql

It defined 15 tables, covering core entities (activity, store, prize, user, draw record) plus order sync logs, operation logs, and risk‑control blacklists, resembling an enterprise deliverable.

Codex’s layout:

lottery_mvp_design/
├── mvp.md
└── mysql_schema.sql

It produced 12 tables that still covered the essential flow (store, backend account, activity, QR code, prize, WeChat user, order, draw record, redemption, risk event, operation log). The design is lighter but sufficient for an MVP.

Non‑Functional Requirements and Architecture Choices

The author asked both agents to consider scalability, performance, availability, reliability, security, maintainability, and deployability.

Cursor recommended a modular monolith for the MVP, avoiding premature micro‑service decomposition, and gave concrete judgments:

2000 stores do not need sharding at the start.

Reliability outweighs performance for a lottery system.

Enforce one‑draw‑per‑order with a unique DB key.

Prize inventory should not rely solely on code checks.

Micro‑service split depends on business volume and system complexity.

Codex also suggested a modular monolith with MySQL and Redis, but presented each non‑functional point as a brief analysis rather than a decisive rule. The author found Cursor’s concise judgments more useful for immediate architecture decisions.

Mutual Evaluation

In a “no‑optimism” prompt, each agent evaluated the other.

Cursor’s view of Codex:

Focuses on terminal agents, sandboxing, headless execution, JSON event streams, CI auto‑repair.

Codex’s value lies in becoming an orchestratable, auditable, permission‑controlled engineering foundation.

Risks include unproven CI‑auto‑repair, vendor lock‑in to OpenAI, and potential product roadmap changes.

Codex’s view of Cursor:

Emphasizes IDE and code‑context awareness.

Cursor’s biggest strength is its proximity to files, cursor, diffs, errors, and terminals—essentially the developer’s real work context.

However, Cursor’s advantage is currently a product‑experience edge, not a long‑term technical moat; future competition from VS Code, JetBrains, GitHub, OpenAI, Anthropic could erode it unless deeper work on context, permissions, security, and collaboration is added.

Token Consumption

Images show token usage for both agents, but the author cautions that direct comparison is unfair because model versions, inference strength, and output length differ.

Conclusion

Overall impressions:

Cursor fits daily IDE workflows: editing files, generating formal deliverables (PRDs, schema, architecture docs).

Codex behaves like a terminal‑based coding agent: natural explanations, iterative file edits, command execution, and CI automation.

For quick, well‑structured documentation, Cursor is faster; for command‑line automation, batch modifications, and CI‑centric scenarios, Codex offers more promise.

Short‑term, Cursor feels like a handy assistant; Codex appears as an emerging engineering agent still maturing.