350 Million Tokens in 11 Days: My Codex Usage Record

Over the past 11 days, I've been using Codex heavily for daily development. Before I knew it, my token usage had climbed to 350 million. Here's a breakdown of what happened behind that number.

The Numbers

• Period: March 30 – April 9, 2026 (11 days)
• Total Token Consumption: ~350 million
• Models Used: Codex 5.4 (primary), Codex 5.3
• Main Use Case: Daily coding and documentation

How I Used the Models

The two models served distinct purposes:

• Codex 5.4: Used for writing documentation — higher quality output, more accurate expression
• Codex 5.3: Used for rewriting and iterating on top of 5.4's output

My actual workflow: draft with Codex 5.4 first, then rewrite and refine with Codex 5.3, followed by repeated review cycles — having the AI review its own writing, suggest changes, and iterate. This "write-review-revise" loop consumed the bulk of the 350 million tokens.

What Ate All Those Tokens

350 million sounds like a lot, and honestly, most of it came from:

• Implementing and iterating on complex features
• Code review and refactoring suggestions
• Long-context conversations (feeding entire files to the AI for context)

The productivity gains from AI-assisted coding are real, but the token cost is hard to ignore. Especially with the documentation workflow — the "write-review-revise" cycle produces high-quality docs, but tokens pile up fast. I need to think about optimizing this process: reducing unnecessary review rounds, managing context length, and keeping token usage in check without sacrificing output quality.

The Optimization Plan: Enter RTK

Starting today, I'm introducing RTK to cut down on wasted tokens. RTK is a CLI proxy that filters and compresses command output before it reaches the LLM, claiming to reduce token consumption by 60-90%.

It handles common dev command outputs — git diff, git log, cat, grep, test results, and more — stripping out the noise and keeping only what matters.

How It Works

RTK transparently rewrites Bash commands via hooks (e.g., turning git status into rtk git status). The AI tool never sees the rewrite — it just receives compressed output.

It applies four strategies per command type:

• Smart Filtering — Removes noise (comments, whitespace, boilerplate)
• Grouping — Aggregates similar items (files by directory, errors by type)
• Truncation — Keeps relevant context, cuts redundancy
• Deduplication — Collapses repeated log lines with counts

It supports Claude Code, Codex, Copilot, Gemini CLI, Cursor, Windsurf, Cline, and more — one command to set up.

Over the next week, I'll run a real-world development test comparing token usage with and without RTK using the same workflow. Results will be shared in a follow-up post.