AI Coding Cost: Claude Code vs Codex vs Gemini

Price-per-token comparisons look clean in blog posts and useless in real life. The reason: Claude Code, Codex CLI, and Gemini CLI serve different task types, generate different output lengths, and run on different model architectures — so the same work costs different amounts on each tool even if the headline token prices were identical. What you actually want to know is: given how you use AI coding tools, which one delivers the most value per dollar? That's a question only your logs can answer.

The price-per-token scorecard

The table below shows approximate API pricing for the primary models behind each tool as of mid-2026. Prices change; always verify on each provider's pricing page. These are listed here so the comparison is grounded, not because you should optimize for the cheapest headline rate.

The flat-plan breakeven

If you're on a flat subscription, the per-token price is irrelevant until you know whether the subscription is saving you money compared to API pay-as-you-go. The breakeven equation is the same for all three tools:

A developer who burns 200M Claude Sonnet tokens per month has an API-equivalent cost of roughly $60 in input and $130 in output (at illustrative rates), totaling around $190. On the $100 Claude Max plan, that's a $90 savings. On the $20 Pro plan, they've already exceeded the threshold — but Pro may have usage limits that kick in before 200M tokens. The point: run the math for your own usage.

Where does YOUR spend go across tools?

The best way to run this comparison isn't with marketing numbers — it's with your own logs. Whoburnedmore reads the usage logs all three tools keep locally and shows you a cost breakdown across all of them in a single run. Here's what that looks like for a developer who uses all three tools regularly:

$ npx whoburnedmore↳ scanning Claude Code, Codex, Gemini CLI logs…   ALL TOOLS  (last 30 days)  tool            tokens       est. cost    % of total  ──────────────────────────────────────────────────────  claude code     284.1M       $61.20       58%  codex cli        88.4M       $33.80       32%  gemini cli       41.2M       $10.60       10%  ──────────────────────────────────────────────────────  total           413.7M      $105.60      100%

In this example, Gemini burns 10% of the total cost despite handling 10% of the token volume — similar unit economics to Claude Haiku. Codex at 32% of cost handles 21% of the token volume, which reflects the reasoning-token overhead on o3 sessions. This breakdown is something you cannot get from any provider's own dashboard, because none of them show you the other tools.

How each tool compares on task types

Cost per token is less informative than cost per task. Different tools shine on different work, which means the cheapest tool for a given task isn't always the one with the lowest token price:

Why you probably don't need to pick one tool

The developers with the best cost efficiency aren't using a single AI coding tool — they're routing tasks to whichever tool handles that task category cheapest. Claude Code for interactive session work, Gemini Flash for quick lookups, Codex o4-mini for autonomous multi-step tasks. Whoburnedmore reads all three simultaneously, so your aggregate cost is always visible even when you're mixing tools.

The honest answer: measure, don't estimate

Every comparison in this guide uses illustrative numbers. Your actual cost on any of these tools depends on what you ask it to do, which models you use, how long your sessions run, and whether you clear context between tasks. The only way to get numbers that apply to you specifically is to run all three tools and measure the output.

The winner of the “cheapest AI coding tool” contest changes based on your workflow. The real question is which tool gives you the best output per dollar you actually spent — and that's only answerable once you've measured all three. 🔥