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Claude Opus 4.6 vs GPT-5.3 Codex
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Claude_Opus_4_6_vs_GPT-5_3_Codex.md

Claude Opus 4.6 vs GPT-5.3 Codex

Source

This is a summary of a YouTube video featuring Greg and Morgan Linton comparing the newly released Claude Opus 4.6 and GPT-5.3 Codex models. Watch the original video

Overview

On a major AI release day, both Anthropic and OpenAI dropped competing coding models. This video provides a deep technical comparison between Claude Opus 4.6 and GPT-5.3 Codex through a practical head-to-head test: building a PolyMarket competitor from scratch. Rather than declaring a single winner, the hosts reveal that these models represent fundamentally different coding philosophies and use cases.

Key Topics

Getting Started with Opus 4.6

Critical Setup Steps:

  • Update to version 2.1.32+ using npm update or claude update
  • Navigate to ~/.claude/settings.json and configure:
    • Set model to "opus" or "claude-opus-4-6" for specificity
    • Enable Agent Teams by adding: "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  • Verify you're running the correct version with /model command

API Users: Opus 4.6 introduces "Adaptive Thinking" with effort levels (including "max" for unlimited thinking depth - only available in 4.6)

Philosophical Differences: Two Approaches to AI Coding

The models diverge fundamentally in their engineering philosophy:

GPT-5.3 Codex: Interactive collaborator approach

  • Tight human-in-the-loop control
  • Mid-execution steering capabilities
  • Real-time course correction as it works
  • "Founding engineer" personality - builds fast and iterates

Claude Opus 4.6: Autonomous agentic approach

  • Plans deeply and runs longer
  • Multi-agent orchestration with parallel research teams
  • Less human intervention required
  • "Senior/staff engineer" personality - thorough and thoughtful

Technical Specifications Comparison

Context Windows:

  • Opus 4.6: 1 million tokens (designed for "load the whole universe and reason over it")
  • GPT-5.3 Codex: ~200,000 tokens (optimized for progressive execution)

Coding Performance:

  • Codex won on SWE Bench Pro and Terminal Bench
  • Opus 4.6 excels at code comprehension, architectural refactors, and reducing "YOLO write code" behavior
  • Codex better for end-to-end app generation and rapid prototyping

The PolyMarket Challenge: Head-to-Head Test

Different Prompts for Different Models:

For Opus 4.6: "Build a competitive polymarket. Create an agent team to explore this from different angles: one on technical architecture, one on understanding polymarket, one on UX, and one on building tests."

For Codex 5.3: "Build a competitive polymarket. Think deeply about technical architecture, understanding polymarket, good clean UX, and building good tests."

Results:

  • Codex completed in 3 minutes 47 seconds with 10 tests passing
  • Opus took significantly longer but created 96 comprehensive tests and a more polished result
  • Codex built functional trading engine with REST API but basic design
  • Opus created four specialized agents (architecture, domain expert, UX design, testing) that researched in parallel before building

Agent Teams: Opus 4.6's Killer Feature

The multi-agent orchestration demonstrated remarkable sophistication:

  • Four agents conducted parallel web research on prediction markets, architecture patterns, UX best practices, and testing strategies
  • Each agent used 25,000+ tokens independently
  • Total token usage: 150,000-250,000 (roughly $20 on Claude Max plan)
  • Agents synthesized findings before implementation began

Token Economics: With agent teams, token usage multiplies by the number of agents - potentially advantageous for Anthropic's business model but delivers more thorough results.

Design Iteration: Mid-Stream Steering Test

Testing Codex's signature feature - the ability to interrupt and redirect:

  • Initial prompt: "Can you spruce it up and make it look nicer?"
  • After minimal changes: "I was looking for a CAPS LOCK MAJOR upgrade... pretend you are Jack Dorsey"
  • Codex paused when questioned, requiring explicit "continue" command (UX quirk)
  • Successfully incorporated Jack Dorsey's "minimal restraint and interaction focused" design philosophy
  • Final result still fell short of Opus's initial design quality

The Verdict

In this specific test: Opus 4.6 won due to:

  • Superior design aesthetic and polish
  • More comprehensive testing (96 vs 10 tests)
  • Better populated seed data and realistic market examples
  • Cleaner, more professional UI (final reveal)

However, Codex built 20x faster and demonstrated impressive mid-stream steering capabilities.

Key Takeaways

  1. Use Both Models: They excel at different tasks. Codex for rapid prototyping and pair programming; Opus for complex, multi-faceted projects requiring deep analysis.

  2. Token Awareness: Opus with agent teams is token-hungry (100,000+ tokens not uncommon). Budget accordingly - Claude Max gives ~10 million Opus tokens/month.

  3. Enable Agent Teams: Don't forget CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS: "1" in your settings.json or you'll miss Opus 4.6's most powerful feature.

  4. For Beginners: Codex might be slightly better for non-technical vibers (better coding benchmarks), but Opus 4.6 produces fewer hallucinations and "YOLO" mistakes.

  5. Team Adoption: Let engineering teams experiment with both. Different developers and different projects will benefit from different approaches.

  6. Methodology Matters: Your preferred workflow determines which model fits better - tight collaboration vs. autonomous delegation.

Action Items

  • Update Claude Code CLI to 2.1.32+ immediately
  • Configure ~/.claude/settings.json with Opus 4.6 and agent teams enabled
  • Try building the same project with both models to understand their strengths
  • Explore the official agent orchestration documentation for advanced configuration
  • Follow Morgan Linton on X for more vibe coding insights
  • Consider getting Claude Max plan if using agent teams extensively (token multiplication factor)

🤖 The era of competing AI coding philosophies has arrived. Rather than converging, these models are diverging - and that's actually a good thing for developers who can now choose the right tool for each job.

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