From February to October 2026, my AI coding agents used 235 billion tokens. At API list prices, that usage is worth about $124,000. The subscriptions that ran it cost about $4,000. That is about 31 times the value at list prices.
This article shows how I measured it, where the value came from, and when a team should still pay for metered API access.
The numbers
| February – October 2026 | |
|---|---|
| Tokens | 235 billion (197 billion in 1.5 million Claude messages, 38 billion on Codex) |
| Value at API list prices | about $124,000 (Claude about $103,600, Codex about $20,100) |
| Subscriptions | about $4,000 (20 seat-months at $200: one Claude seat Feb – Sep, one Codex seat Aug – Sep, then nine employer-paid Claude seats and one Codex seat in October) |
| Ratio | about 31× |
| October 2026 only | $48,500 of usage on $2,000 of seats, about 24× |
The usage covers my Claude and Codex agent work: a regulated crypto platform where I am CTO, a streaming platform rebuild, and my own video studio. The Claude agents ran in Claude Code and the Codex agents ran in the Codex CLI, each signed in to a flat-rate plan instead of an API key.
How I measured it
Both tools keep a local transcript of every session, with the token counts of each model response. I wrote a counter that reads every transcript on the three machines that ran agents, including the compressed archives.
- Duplicates. Agents copied transcripts between machines, so the counter keeps each message once, by its message id.
- Prices. Each message is priced at the published list price of its model: input, output, cache writes (at 1.25 or 2 times input, by cache duration) and cache reads. Where a provider does not list a cached-input price, I used 0.1 times input.
- Seats. Until September I ran on one $200 Claude plan, plus one $200 Codex plan from August. In October the team moved to nine employer-paid Claude seats, with one Codex plan alongside. Each seat is priced at $200.
The result is an estimate in both directions:
- The value is low. Claude Code deletes transcripts after about 30 days by default. Much of the work before September is gone, so early months show only a fraction of the real use.
- The spend leaves some costs out. Some Codex work used paid credits beyond the seat price, which this total does not include. DeepSeek ran on its own pay-per-token API and is excluded.
I report “about 31×” for that reason, as an estimate, not a floor or a ceiling.
Where the value came from
Context, not output. About 97% of the Claude tokens were cache reads: the agent re-reading its instructions, the files it opened and the conversation so far, on every turn. Output tokens were about 0.3%.
That share decides the economics. On a metered API, the cost of a long agent session grows with every turn because each turn re-reads the history, even at the cache price. On a flat-rate plan, the same re-reading counts against a usage window, not a bill.
The strongest model did the most work. Claude Opus 5.5 alone was about $47,300 of the value, 38% of the total. It planned, reviewed and handled the changes that touch money, identity checks and sign-in. On a metered API, I would have pushed much of that work to cheaper models for cost alone, not for quality.
What flat-rate plans cost in other ways
A subscription is not free capacity. It moves the limit from money to time.
- Usage windows. Each account has a five-hour window and a weekly limit. A guard holds new background agents when an account reaches 90% of its window, and lanes wait for the reset.
- Context still matters. Because cache reads dominate, a bloated agent burns its window faster. Short briefs and small, one-item builder tasks keep each window productive.
- Cheap models still matter. A small model as builder uses a fraction of the window that a large one uses. In one 41-item pilot, the small builder also had the fewest review misses.
When to pay for the API instead
Flat-rate plans fit agents that a person runs for their own engineering work, inside the provider’s official tools and within the plan’s limits. Use metered API access when:
- The model serves your customers. A product feature needs its own key, rate limits and billing, not a person’s seat.
- The workload runs unattended at scale. Batch jobs and pipelines with no person behind them belong on the API.
- You need guarantees. API plans give the service levels, data terms and spend controls that a team plan may not.
Read the current terms of each plan before you build on it. Plans and limits change often.
What I would tell a team
- Measure before you choose. Run your agents for a month and price the transcripts. The ratio depends on how much your agents re-read.
- Keep transcripts longer than 30 days if you want to measure later. I lost most of my early data to the default.
- Treat usage windows as a scheduling problem. Schedule long jobs across hours, and stop new work before a window runs out.
- Spend the savings on quality. Use the strong model for planning and review, where its judgment pays.
Want help setting up an agent fleet that stays inside its budget? Email [email protected].
