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How to Use GPT and Claude Together: Multi-Agent Coding Workflow Guide

Cowrie
Cowrie
Dev @ Bswen

I used to juggle between GPT and Claude, wondering which one to pick for each task. Then I realized the question was wrong. Instead of choosing one, I started using both together in a structured workflow. The result? I went from handling one task at a time to managing three or four simultaneously. Here’s the workflow that changed how I code with AI.

The Core Idea

Different AI models excel at different things. GPT-5.4 with Codex is a powerhouse for implementation. Claude Opus 4.6 shines at code review and debugging. ChatGPT 5.4 in the browser offers a fresh perspective on architecture. Using all three transforms you from a tactical coder into an AI orchestra conductor.

The key insight: each model operates on the same codebase from different angles. This reduces blind spots and improves overall quality.

The Three-Phase Workflow

Here’s the workflow I use:

Three-Phase Multi-Agent Workflow
+-------------------+     +-------------------+     +-------------------+
|   PHASE 1         |     |   PHASE 2         |     |   PHASE 3         |
|   IMPLEMENTATION  | --> |   AUDIT           | --> |   ARCHITECTURE    |
+-------------------+     +-------------------+     +-------------------+
|                   |     |                   |     |                   |
|  GPT-5.4 Codex    |     |  Claude Opus 4.6  |     |  ChatGPT 5.4      |
|                   |     |                   |     |                   |
|  - Write code     |     |  - Find bugs      |     |  - Validate design|
|  - Generate tests |     |  - Debug problems |     |  - Plan ahead     |
|  - Refactor       |     |  - Review quality |     |  - Strategic view |
+-------------------+     +-------------------+     +-------------------+

Let me break down each phase.

Phase 1: Implementation with GPT-5.4 Codex

GPT-5.4 excels at getting things done fast. With the merged Codex capabilities and a 1M token context window, it handles whole repositories without losing context.

Why GPT for Implementation

GPT-5.4 Implementation Strengths
+------------------------+----------------------------------------------+
| Strength               | What It Means for You                        |
+------------------------+----------------------------------------------+
| Speed                  | Generates complete features in minutes       |
| Whole-repo context     | Understands your entire codebase at once     |
| Reduced laziness       | Actually implements what you ask             |
| Thorough execution     | Follows through on complex implementations   |
| IDE integration        | Works seamlessly in VS Code via Codex        |
+------------------------+----------------------------------------------+

How to Use It

  1. Open your project in VS Code with the Codex extension
  2. Give clear, specific requirements upfront
  3. Use whole-repository context when available
  4. Review output before committing (that’s Phase 2)

Example Prompts for Implementation

Implementation Prompt Examples
Feature Development:
"Implement user authentication with JWT tokens, including login,
logout, and token refresh endpoints. Use the existing database
schema in /src/db/schema.ts."


Refactoring:
"Refactor the payment processing module to use the new payment
gateway API. Update all related files and maintain backward
compatibility."


New Feature:
"Add a rate limiting middleware that limits requests per user
to 100 per minute. Store counts in Redis using the existing
connection pool."

What GPT Does Well

  • Building complete features from specifications
  • Repository-wide refactoring
  • Cross-file bug fixes
  • New project scaffolding (from simple websites to complex iOS apps)
  • Writing initial tests alongside code

Phase 2: Audit and Debug with Claude Opus 4.6

Once GPT generates code, I hand it off to Claude for review. Claude excels at finding edge cases, security issues, and subtle bugs that GPT might miss.

Why Claude for Auditing

Claude Opus 4.6 Audit Strengths
+------------------------+----------------------------------------------+
| Strength               | What It Means for You                        |
+------------------------+----------------------------------------------+
| Nuanced understanding  | Catches edge cases others miss               |
| Self-debugging         | Iterates on problems until solved            |
| Long context           | Maintains context through long reviews       |
| Quality explanation    | Explains why something is wrong              |
| Architectural eye      | Sees patterns and anti-patterns              |
+------------------------+----------------------------------------------+

How to Use It

  1. Share the full context from Phase 1
  2. Ask specific questions about potential issues
  3. Request validation against best practices
  4. Have Claude explain any problems found

Example Prompts for Auditing

Audit Prompt Examples
Security Review:
"Review the authentication implementation for security
vulnerabilities. Check for common attack vectors like SQL
injection, XSS, and session hijacking."


Quality Check:
"Identify any potential bugs or edge cases in this payment
processing code. What happens if the payment gateway times out?"


Architecture Validation:
"Does this implementation follow SOLID principles? Where could
it be improved for maintainability?"


Edge Case Hunting:
"What happens if the database connection fails during this
transaction? Are all error paths handled correctly?"

What Claude Catches

  • Security vulnerabilities (XSS, SQL injection, auth flaws)
  • Unhandled edge cases and error paths
  • Performance bottlenecks
  • Code that violates SOLID principles
  • Hidden dependencies between components

Phase 3: Architecture with ChatGPT 5.4

The final phase uses ChatGPT in the browser to reason about the big picture. Without the coding agent scaffold, it approaches problems differently.

Why ChatGPT for Architecture

ChatGPT 5.4 Architecture Strengths
+------------------------+----------------------------------------------+
| Strength               | What It Means for You                        |
+------------------------+----------------------------------------------+
| Broader knowledge      | Connects patterns across domains             |
| Design perspective     | Reasons about trade-offs without code bias   |
| System thinking        | Sees the forest, not just trees              |
| Fresh viewpoint        | Different from coding-focused interfaces     |
+------------------------+----------------------------------------------+

How to Use It

  1. Upload your project as a zip file
  2. Ask architectural questions
  3. Discuss trade-offs between approaches
  4. **Plan future improvements

Example Prompts for Architecture

Architecture Prompt Examples
Design Decisions:
"Given this codebase structure, what are the trade-offs between
adding a microservice vs keeping this as a module in the monolith?"


Scalability Planning:
"How should I structure the data layer to handle 10x the current
traffic? What patterns would you recommend?"


Pattern Evaluation:
"Is the current repository pattern the right choice, or would
Active Record be better for this use case? Why?"


Future-Proofing:
"What architectural changes should I plan for if we need to
support multi-tenancy in 6 months?"

Model Strengths Comparison

Here’s a quick reference for when to use each model:

Model Strengths Quick Reference
+-------------------+-------------------+-------------------+-------------------+
| Task              | GPT-5.4 Codex     | Claude Opus 4.6   | ChatGPT 5.4       |
+-------------------+-------------------+-------------------+-------------------+
| Write new code    | BEST              | Good              | Fair              |
| Find bugs         | Good              | BEST              | Fair              |
| Debug issues      | Good              | BEST              | Fair              |
| Architecture      | Good              | Good              | BEST              |
| Speed             | BEST              | Good              | Good              |
| Quality review    | Fair              | BEST              | Good              |
| Edge cases        | Fair              | BEST              | Good              |
| System design     | Good              | Good              | BEST              |
+-------------------+-------------------+-------------------+-------------------+

Putting It All Together: A Real Example

Let me show you how this works in practice. Say I’m building a user authentication system.

Step 1: GPT Implementation

I open VS Code with Codex and give this prompt:

Step 1: Implementation Prompt
"Implement a complete authentication system with:
- Email/password login with bcrypt hashing
- JWT tokens with 15-minute expiry
- Refresh tokens stored in Redis
- Rate limiting on login attempts
- Password reset via email


Use the existing Express.js setup and PostgreSQL database."

GPT generates the implementation across multiple files in about 5 minutes.

Step 2: Claude Review

I share the generated code with Claude:

Step 2: Audit Prompt
"Review this authentication implementation for:
1. Security vulnerabilities (OWASP Top 10)
2. Edge cases in the token refresh flow
3. Error handling completeness
4. Any potential race conditions"

Claude identifies that the refresh token rotation has a race condition and the rate limiting doesn’t account for distributed instances. I go back to GPT to fix these.

Step 3: Architecture Discussion

I zip the project and upload to ChatGPT:

Step 3: Architecture Prompt
"Review the overall authentication architecture. Should we consider:
- Adding OAuth providers?
- Implementing multi-device session management?
- Using a different token strategy for mobile vs web?"

ChatGPT helps me plan the roadmap for future improvements and validates the current design.

Why This Workflow Works

The power comes from multiple perspectives:

Benefits of Multi-Agent Approach
+-------------------+-----------------------------------------------+
| Benefit           | Explanation                                   |
+-------------------+-----------------------------------------------+
| Reduced blind     | Each model sees things others miss            |
| spots             |                                               |
+-------------------+-----------------------------------------------+
| Higher quality    | Code passes through multiple quality gates    |
+-------------------+-----------------------------------------------+
| Better coverage   | Speed, quality, and architecture all covered  |
+-------------------+-----------------------------------------------+
| Increased         | Work on 3-4 tasks simultaneously              |
| capacity          |                                               |
+-------------------+-----------------------------------------------+
| Strategic focus   | You focus on architecture, not implementation |
+-------------------+-----------------------------------------------+

The developer who first shared this approach on Reddit said it best: “For me it really has solved agentic software engineering. I can work on 3-4 things at the same time. I can really focus on the big picture, the tech stack, architecture, systems, and overall code structure now.”

Common Patterns

Pattern 1: Sequential Pipeline

Use this for new feature development:

Sequential Pipeline Pattern
[GPT Codex] --> [Claude Opus] --> [ChatGPT]
   |                 |                |
Implement         Review          Validate

Pattern 2: Parallel Review

Use this for critical code:

Parallel Review Pattern
                    +-- [GPT Codex: Implementation]
                    |
[Codebase Context] -+-- [Claude Opus: Security Audit]
                    |
                    +-- [ChatGPT: Architecture Review]

Pattern 3: Iterative Loop

Use this for complex projects:

Iterative Loop Pattern
[GPT Codex] <--> [Claude Opus]
     |                |
  Implement        Review
     |                |
     +----- Fix ------+
              |
         [ChatGPT: Validate]
              |
         [GPT Codex: Next Feature]

Cost Considerations

Yes, using multiple models costs more. Here’s how to optimize:

Cost Optimization Strategies
+-------------------+-------------------+---------------------------+
| Phase             | Primary Model     | Cost-Effective Alternative|
+-------------------+-------------------+---------------------------+
| Implementation    | GPT-5.4 Codex     | GPT-5.3 Codex             |
| Code Review       | Claude Opus 4.6   | Claude Sonnet 4.6         |
| Architecture      | ChatGPT 5.4       | GPT-5.4 API               |
| Quick Checks      | Claude Sonnet 4.6 | GPT-5.4                   |
+-------------------+-------------------+---------------------------+

Tips to save costs:

  • Use Sonnet for most reviews, Opus only for critical code
  • Cache context across phases where possible
  • Batch similar operations
  • Skip Phase 3 for smaller features

Getting Started

Don’t try to adopt everything at once. Start here:

  1. Week 1: Use GPT for implementation, Claude for review
  2. Week 2: Add ChatGPT architecture sessions for larger features
  3. Week 3: Experiment with different patterns (sequential vs parallel)
  4. Week 4: Customize based on what works for your projects

Common Pitfalls

Pitfalls to Avoid
+------------------------+-----------------------------------------------+
| Pitfall                | Solution                                      |
+------------------------+-----------------------------------------------+
| Context loss between   | Use structured handoff notes, keep shared     |
| phases                 | context files                                 |
+------------------------+-----------------------------------------------+
| Redundant work across  | Define clear phase boundaries, avoid asking   |
| models                 | same questions                                |
+------------------------+-----------------------------------------------+
| Workflow too complex   | Start with 2 models, add third only when      |
|                        | needed                                        |
+------------------------+-----------------------------------------------+
| Model selection        | Follow the three-phase pattern, iterate       |
| paralysis              | based on results                              |
+------------------------+-----------------------------------------------+

The Bottom Line

Using GPT and Claude together transforms AI-assisted development. Instead of asking “which model should I use?”, I now ask “which phase of work am I in?” The answer determines the model.

This approach shifts your role from tactical coder to strategic conductor. You focus on architecture, requirements, and system design while the models handle implementation and quality assurance. The result is higher quality code, faster delivery, and more time for the interesting problems.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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