"Which one is the smartest?" Asking this question means you're using AI wrong.
Don't pick just one. Use each for its strengths.
For thinking: Claude Code
For execution: Codex
For polishing Japanese: Gemini
Their specialties are clearly divided.
Using them incorrectly lowers output quality even with the same prompt. But matching tasks to tools lets all three perform at their best.
This guide explains each model's strengths and my workflow. We'll cover:
① Claude Code
② Codex
③ Gemini
④ How to run a task through all three
Bookmark this to master the workflow.
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Now, let's begin.
① Claude Code Fable: Thinking and Planning
I delegate all "thinking" work to Claude Code, specifically the Fable model.
It decides what to build, defines the end goal, structures the steps, and breaks down tasks so sub-agents can execute without confusion. This is where Fable shines.
Best for Starting from Zero
Fable excels when the answer isn't defined yet.
If the task is clear, any model works similarly. The difference appears in defining problems or planning from scratch. Here, Fable is overwhelmingly strong.
For example, if I ask, "I want a tool to automate morning research," without specifying details, Fable analyzes time sinks, separates AI vs. human tasks, lists deliverables, and suggests an order. Then implementation can be handed off.
Ideal Orchestrator
It's also excellent at breaking down work and assigning it to other AIs.
It maintains a holistic view, tracking progress and ensuring decisions align. It knows how granularly to split tasks for sub-agents. This advantage grows with complex, parallel workflows.
So I use Claude Code as the command center for planning and delegation.
Tasks for Claude Code
Implementation:
- Requirements definition for new tools
- Deciding implementation strategy
- Debugging root causes
- Breaking down tasks for sub-agents
Creation:
- Planning X articles, notes, newsletters (theme & angle)
- Defining LP selling points
- Drafting body text
② Codex Astra: Implementation and UI Control
I delegate all "hands-on" work to Codex, using the Astra model.
Writing code, building tools, controlling browsers, generating images—Codex turns Claude's plans into reality.
Turning Specs into Reality Fast
Astra's core strength is implementation.
Given clear specs, it's incredibly fast. Building tools, adding features, fixing bugs—these finish quicker with Codex than by making Claude think too much.
It handles long sessions well, maintaining focus across multiple files until completion. So once requirements are set, I hand it off entirely.
Direct Screen Interaction
Computer Use is another pillar.
Astra can directly operate screens like a human using mouse/keyboard.
It views and interacts with interfaces effortlessly. It opens pages to check layout, fills forms to test submission, reads error screens to fix issues, logs into admin panels, and completes application forms.
This eliminates manual work previously done by humans. You'll feel a dramatic reduction in workload beyond just coding.
Performance metrics confirm this: On OpenAI's OSWorld 2.0 benchmark, Astra scores 72.6% (up from 65.7%). Time per task dropped from ~75 to ~40 minutes. Browser control speed on Mind2Web improved 1.9x.
Image Generation Too
Generating images within the same chat flow is powerful.
Diagrams, thumbnails, slide assets—all created without switching tools.
Tip: Don't generate one-by-one. Define the overall structure first, then batch-generate to maintain consistent style and tone.
Tasks for Codex
- Coding, tool building, website creation
- Browser operations (forms, admin updates)
- Generating diagrams, thumbnails, slide assets
- Verifying outputs and fixing errors via screen inspection
Tips for Delegation
Codex runs autonomously, so prompting matters.
- Define completion criteria clearly to prevent stopping early or overstepping.
- Don't interrupt mid-task. Unlike Claude, which benefits from iterative thinking, Codex is faster when left alone. Checking progress implies unclear goals; resolve ambiguities beforehand.
③ Gemini 3.8 Flash: Polishing Japanese
Finally, Gemini 3.8 Flash polishes the text. It leads in natural Japanese phrasing.
Removing AI Stiffness
AI-written text is understandable but feels unnatural.
Wordy phrases, redundancies, overloaded sentences, monotonous endings—these create that "AI vibe."
Gemini 3.8 Flash smooths these out. Long sentences get cut appropriately, rhythm improves, and the result reads naturally.
So I separate "writing" from "editing." Fable/Astra draft; 3.8 Flash refines. Self-editing misses personal quirks.
Define Editing Scope
Without instructions, meaning might change unintentionally.
E.g., changing "might support" to "will support" alters commitment levels. Numbers/names shouldn't be paraphrased.
Specify allowed changes: Fix stiff phrasing, redundancy, sentence length. Forbid changes to meaning, numbers, proper nouns, section order. Request a list of changes to verify against original text via Claude.
No Extra API Costs
Call Gemini 3.8 Flash via Antigravity CLI or Cursor CLI.
These tools include usage in subscription fees, no extra API costs.
④ Running One Task Through All Three
Here are two real-world workflows.
Writing an X Article
- Claude Code: Decide theme/angle, select materials
- Claude Code: Structure article, determine reading flow
- Claude Code: Write draft, fact-check
- Gemini: Polish Japanese, list changes
- Claude Code: Verify meaning unchanged against original
- Codex: Generate diagrams/thumbnails
Structure/writing: Claude. Editing: Gemini. Images: Codex. Human checks only at start (theme) and end (publish).
Building an LP
- Claude Code: Define selling points/requirements, structure page
- Claude Code: Write sales copy
- Gemini: Polish copy
- Codex: Code webpage, test display/form submission via browser
- Claude Code: Final consistency check
Codex handles both implementation and verification, auto-fixing layout/form issues by viewing the screen.
What Changes When Using All Three
Recap strengths:
- Claude Code: Think, plan, delegate
- Codex: Implement, control UI, generate images
- Gemini: Polish Japanese
No single model dominates everything. Match tasks to tools.
Trying to make one AI do everything limits potential. By assigning tasks based on strengths, output density dramatically increases in the same time frame.
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