Claude Code vs Codex: 36 vs 28 files, $2.50 vs $2.04, infinite loop caught — real-world comparison
Someone on r/ClaudeAI ran a head-to-head comparison of Claude Code and Codex (via Cursor) on two practical tasks—same prompts, same MCP setup (GitHub + Slack), same machine. No benchmarks, real builds.
Tasks
- Task 1: PR triage bot — Read open PRs, score by complexity (files ×2, lines/10, +3 for no labels, +5 for no reviewers), write a markdown report, post Slack alerts for high scores. Required retries, error logging, strict TypeScript, no
any. - Task 2: Real-time code review UI — React + TypeScript, WebSockets, inline comment threads, optimistic updates with rollback, virtualized diff viewer, WS reconnect with exponential backoff. No UI libraries.
Claude Code results
- Ran
/mcpto verify tools before writing code - Built 36 files in ~12 minutes
- Wrote an unprompted two-client WebSocket smoke test (broadcast: 3ms)
- Zero
any, passed typecheck first try - UI worked immediately
Codex (via Cursor) results
- Failed Task 1: GitHub MCP wasn't reachable through Cursor's execution path. Handled it cleanly (retried 3x, logged errors, didn't crash), but no delivery.
- Task 2: Shipped a working UI in ~15 minutes, smoke test passed at 5ms
- Hit TypeScript errors on first compile and an infinite React loop (
useEffectcalling hydrate repeatedly). Needed a ref guard patch. - 28 files, more compact architecture
Cost (estimated, both tasks)
- Claude: ~$2.50
- Codex: ~$2.04
- Difference: ~18-23%
Takeaways
Neither agent “won”. Claude feels like pairing with someone who verifies everything before touching the keyboard. Codex feels like a senior dev who wants to ship and move on. Both got WebSocket broadcast under 10ms—six months ago that wasn't a given. No any leaks, no hallucinated tool names.
📖 Read the full source: r/ClaudeAI
👀 See Also
AI Functions: Runtime Code Generation with Automated Verification
AI Functions is a Python library that lets you define functions with natural language specifications instead of implementation code, executes LLM-generated code at runtime, and validates outputs with post-conditions that trigger automatic retries on failure.
SubQ: A Sub-Quadratic LLM with 12M-Token Context Window
SubQ is a fully sub-quadratic sparse-attention LLM offering a 12M-token context window at 150 tokens/s, with SWE-Bench Verified 81.8% and RULER @ 128K 95.0%. It reduces attention compute ~1000× compared to transformers.
Jobly: Contract Marketplace with AI-First Dispute Resolution and Community Voting
Jobly is a contract marketplace built with Next.js 14, TypeScript, and Supabase, featuring an escrow system with 10% provider bonds on proposals and a dispute pipeline that starts with AI evaluation using Claude, then allows appeals to community stake voting.
Skills Creator Tool for OpenClaw Helps Developers Package Workflows
A developer created a skill called skills-creator that guides users through creating quality skills for OpenClaw, addressing common pitfalls like vague descriptions and documentation-like instructions. It's available on ClawHub and provides a design-driven approach with description formulas, checklists, and complexity tiers.