Tokenomics: Where AI Agents Actually Spend Their Tokens

You drop a GitHub issue into your agentic coding pipeline and wait. Ten minutes later, the PR arrives — working code, tests passing, documentation updated. Then you get the bill.

Token pricing for reasoning models runs anywhere from $2 to$15 per million input tokens. A single agentic software engineering task on GPT-5 can consume over 40 million tokens. That's not a typo — forty million. At current rates, one PR costs somewhere between the price of a coffee and a three-course dinner, depending on your model and provider.

The question that keeps engineering leaders awake: where does it all go?

What the Data Says

A team at Concordia University ran the numbers. Their paper, Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering (Salim et al., arXiv 2601.14470), analysed 30 software development tasks executed by the ChatDev multi-agent framework on GPT-5. They mapped ChatDev's internal phases to the standard Software Development Life Cycle — Design, Coding, Code Completion, Code Review, Testing, and Documentation — then measured exactly where every token was spent.

The results are not what most developers expect.

Code Review Eats 60%% of Your Budget

Development Stage	Average Token Share
Code Review	59.4%%
Code Completion	26.8%% (in affected tasks)
Documentation	20.1%%
Testing	10.3%%
Coding (initial)	8.6%%
Design	2.4%%

The dominant cost is not writing code. It's reviewing code. The programmer and reviewer agents engage in multi-turn dialogue — pointing out issues, suggesting fixes, iterating. Each round passes the full code context back and forth. Across 30 tasks, this single phase consumed nearly 60%% of all tokens.

Initial Coding, meanwhile, accounts for less than 9%%. Design is cheaper still at 2.4%%. The conventional intuition — "generation is expensive, review is cheap" — is inverted.

The Communication Tax

Across all phases, the ratio of input to output tokens sits at roughly 2:1:

Token Type	Average Share
Input	53.9%%