# Apply Technique: Hybrid AI Workflow: Combine Claude Code's Reasoning with Codex's Execution
## What This Is
Leverage a hybrid approach by using Claude Code for complex planning and design tasks where its deep reasoning excels. Then, use the more efficient and autonomous Codex for executing the well-defined tasks, achieving a balance of high-fidelity output and cost-effective implementation.
Source: https://www.leanware.co/insights/codex-vs-claude-code
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://www.leanware.co/insights/codex-vs-claude-code) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Scan the current project to identify two task types: (1) complex, multi-file refactoring or architectural planning suitable for Claude's reasoning, and (2) discrete, single-file bug fixes or API integrations suitable for Codex's execution.
- Create a set of wrapper scripts or shell aliases (e.g., `plan-with-claude`, `build-with-codex`) that allow the user to explicitly invoke either the Claude Code or Codex CLI agent. Ensure these scripts correctly route requests to the respective agent and its configured API.
- For advanced implementation, evaluate using an orchestration framework like OpenCLAW. Generate a basic `claw_config.yml` that defines a two-step workflow: a 'planning' stage using a Claude model provider and an 'execution' stage using an OpenAI model provider, passing the output from the first stage to the second.
## Reference Implementation
```
| Aspect | Claude Code Strength | Codex Strength | Example Workflow[1][2] |
|---------------------|--------------------------------------------------------|---------------------------------------|-----------------------------------------------|
| **Complex Reasoning** | Large context (200K+ tokens), refactors messy code | Token-efficient (~3x less usage) | Claude plans architecture; Codex executes |
| **Autonomy** | Interactive terminal mode | Sandboxed, set-and-forget | Codex for batch ops; Claude reviews output |
| **Benchmarks** | SWE-bench (80.8%), Figma fidelity | Terminal-Bench (75.1%), cost | Hybrid for best results |
```
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it