We have all run into the wall.
It is Tuesday afternoon. You are deep in the flow state on a massive project, refactoring a complex component. You send off a prompt to Claude Code, and then you see it: the dreaded notification that you have maxed out your high-reasoning compute allotted for the week.
For a while, I did not have a solution I was happy with. I could buy another Claude Code subscription, I could pay as you go with the Anthropic API, or I could go use another tool, or I could wait a few days for everything to reset.
I know I am not alone in this. I have read plenty of threads from people juggling multiple Claude accounts just to keep access to Claude top-tier reasoning models.
But last month, when I hit the wall again, I had a realization that changed my workflow and stopped me from buying a redundant subscription or paying for the expensive API credits.
The Inventory Check
When my primary Claude subscription ran dry, I started tool-hopping to finish my work.
First, I remembered I was still paying for OpenAI/ChatGPT. So, I switched to a terminal-based Codex tool. It worked fine for quick scripts.
When I ran out of patience with that, I realized my Google Workspace subscription gave me access to Gemini. So, I switched to the Gemini CLI to handle larger context windows.
I spent two days manually copy-pasting context and jumping between three different AI command-line tools. It was frustrating. I was constantly trying to remember which model was better at regex versus which one could handle reading 20 files at once.
But as I looked at my credit card statement, the absurdity hit me. I was paying for Claude. I was paying for OpenAI. I was paying for Google.
Why on earth would I buy another Claude subscription when I already had two other capable coding assistants sitting idle?
The Shift: From Coder to Manager
The problem was not that I needed more Claude tokens. The problem was that I was wasting my most valuable tokens on cheap work.
Claude Max tier is expensive because of its reasoning capabilities - its ability to architect solutions and understand deep nuance. Using those expensive reasoning tokens to generate 500 lines of React boilerplate or write simple unit tests is economically awful. It is like hiring a Principal Architect to center a div.
I realized I needed to stop treating Claude like a junior developer doing all the typing, and start treating it like an Engineering Manager.
Its job should not be to write all the code; its job should be to understand the problem and delegate the execution to cheaper, faster workers.
The DIY Mixture of Experts
Once my Claude subscription reset on Friday, I stopped coding and started configuring.
I knew Claude could execute terminal commands. I also knew I had CLI access to Gemini and Codex. I decided to automate the manual tool-hopping I had been doing all week.
I opened up my global CLAUDE.md file (the instruction file that governs how Claude behaves in a project) and started teaching it how to delegate.
I did some rough research on strengths:
- Gemini: Great for massive context windows and summarizing large chunks of legacy code.
- Codex (OpenAI): Snappy, compliant, and great for standard scripting tasks.
- Claude: The best at architecture, complex debugging, and orchestration.
I added instructions into CLAUDE.md that essentially said: If the user asks for boilerplate generation, construct a command using the Gemini CLI and pipe the output back to me. If the task is a simple script, call Codex.
The Result
Now, when I spin up a new project, I do not have to think about which tool to use.
I give Claude a high-level goal. Claude analyzes it. If it involves heavy lifting that does not require deep reasoning, Claude automatically writes the CLI command to offload that work to Gemini or Codex, executes it, reads the output, and presents it to me.
Claude has become the orchestration layer.
By offloading the volume coding to the services I am already paying for via Google and OpenAI, my precious Claude Code Max subscription now easily lasts the entire week.
It seems silly now to ever consider buying duplicate subscriptions for the same service. If you are going to spend money, spend it on specialized tools, and use your smartest AI to manage them.
Appendix: The Technical Workflow
Want to implement this Manager-Worker architecture yourself? Here is the exact setup I use to make Claude Code delegate tasks to Gemini and Codex without incurring extra API costs.
1. The Prerequisites
You need the CLI tools installed and authenticated via your user account subscriptions (not API keys).
Gemini CLI:
- Install: Part of the Google Cloud SDK or Gemini Code Assist tools.
- Auth: Run gcloud auth login (or your specific tool login command) to authenticate with your Google Workspace identity. This taps into your existing Workspace quota.
Codex / ChatGPT CLI:
- Install: Use the official OpenAI CLI or a community wrapper (like chatgpt-cli) that supports browser-based login.
- Auth: Authenticate via your ChatGPT Plus/Team account. Most tools will prompt you to log in via a browser window or paste a session token to link your subscription.
2. The CLAUDE.md Configuration
This is the brain of the operation. Add the following section to your project CLAUDE.md (or your global instruction file). This explicitly tells Claude how and when to offload work.
**Mixture of Experts Delegation Strategy**
You act as the **Engineering Manager**. Your primary goal is to architect solutions and verify quality, while conserving your own expensive reasoning tokens by delegating implementation details to external CLI tools.
**The Worker Agents**
**1. Gemini (The Reader)**
- **Role:** Junior Dev / Researcher
- **Best For:** High-context tasks, refactoring large files (>200 lines), summarizing documentation, explanation.
- **Invocation:** cat [filename] | gemini -p "[Detailed instructions on what to do]"
**2. Codex (The Scripter)**
- **Role:** Scripting Specialist
- **Best For:** Regex, writing standalone Python scripts, generating config boilerplate, unit tests.
- **Invocation:** codex -m "[Instructions]" -f [filename if needed]
**Delegation Rules**
1. **Delegate by Default:** Before writing code yourself, assess the complexity. If it is standard boilerplate or a simple refactor, **delegate it**.
2. **Pipe Context:** Never ask a worker to fix the file without piping the file content to them (e.g., via cat).
3. **Managerial Review:** You are responsible for the final quality.
- Execute the worker command.
- **Read** the output from stdout.
- **Verify** it meets the requirements.
- Only *then* write it to the file.
3. How It Works in Practice
The User Prompt:
Claude, I need a Python script to parse our access logs and extract all IPs from the 404 errors.
Claude Internal Monologue (The Manager Logic):
This is a regex/scripting task. It requires low reasoning but strict syntax. I should not waste my context on this. I will assign it to Codex.
Claude Action:
Claude executes the shell command:
codex -m "Write a python script to parse access.log and print IPs associated with 404 errors"
The Result:
Codex generates the script using your ChatGPT subscription. Claude reads it, sees that it looks correct, saves it to parse_logs.py, and reports back to you:
I have had Codex generate the log parser script. I reviewed the regex and it looks accurate for standard Apache logs. File saved.
Token Cost to You:
- Claude: ~50 tokens (reasoning and command generation)
- OpenAI/Google: Covered by your existing monthly subscription
- Total Savings: 90% of your Claude quota preserved
What's Next: Expanding Your AI Team
Once you have this Manager-Worker architecture running smoothly, there are several ways to expand it.
Add GitHub Copilot CLI
GitHub Copilot CLI is another excellent worker to add to your roster. It is included with your existing Copilot subscription and excels at:
- Shell command generation: Ask for complex one-liners and it explains them
- Git workflows: Commit messages, branch management, PR descriptions
- GitHub-specific tasks: Issue triage, PR reviews, Actions debugging
Add it to your CLAUDE.md as a third worker agent for GitHub-centric tasks.
Explore Local Coding Agents
The next frontier is running coding agents locally without any subscription costs. Projects like Ollama, LM Studio, and open-weight models are making this increasingly viable. A local agent could handle:
- Sensitive codebases that cannot touch external APIs
- High-volume repetitive tasks where even free tiers run dry
- Offline development environments
The Manager-Worker pattern translates directly - Claude orchestrates while local models do the heavy lifting.
Get the Tools Set Up
If you have not installed all the CLI tools yet, here are the guides:
- How to Install Claude Code CLI - The manager that orchestrates everything
- How to Install GitHub Copilot CLI - Great for GitHub workflows
- How to Install Google Gemini CLI - Free tier with 1M token context
- How to Install OpenAI Codex CLI - Fast and reliable for scripting
