AI Coding Agent Cost Per Bug Fixed: A Practical Estimation Framework
May 24, 2026 · 6 min read
Cost per Bug Is Better Than Cost per Token
If you use AI coding agents for maintenance, the most useful metric is not cost per token. It is cost per bug fixed. A model can be expensive per million tokens and still be cheap if it fixes the bug in one attempt. A cheaper model can become expensive if it loops through five wrong theories and still needs a human rescue.
This framework helps estimate the real cost of using an AI agent to fix bugs across a codebase. It works for subscription tools, direct APIs, and internal agent platforms as long as you can approximate token usage.
The Formula
A practical estimate looks like this:
Cost per fixed bug = discovery cost + repair cost + verification cost + rework cost, divided by acceptance rate.
| Component | What it includes | Typical token shape |
|---|---|---|
| Discovery | Reading issue, logs, files, and failing code | Input-heavy |
| Repair | Generating patches and explanations | Mixed input/output |
| Verification | Running tests and reading failures | Input-heavy |
| Rework | Second attempts after failed tests | Loop-dependent |
Three Bug Types, Three Cost Profiles
| Bug type | Typical workload | Cost risk |
|---|---|---|
| Known failing test | Read test, inspect code, patch, rerun | Low |
| User-reported UI bug | Reproduce, inspect browser state, patch | Medium |
| Intermittent production bug | Logs, traces, hypotheses, multiple attempts | High |
The same model can be cost-effective for one bug type and wasteful for another. Cheap models are often good for known failing tests. Premium models are more defensible when the bug requires causal reasoning across systems.
Example: A Medium UI Bug
Suppose a user reports that a settings form saves but does not update the dashboard. The agent reads the issue, inspects the form component, API route, state hook, and dashboard component, then writes a patch and runs tests. A realistic workload might be 350K input tokens and 70K output tokens. If the first fix fails, a second loop adds 180K input and 35K output tokens.
| Model | First attempt | With one rework loop |
|---|---|---|
| Claude Sonnet 4.6 ($3/$15) | $2.10 | $3.17 |
| Claude Opus 4.7 ($5/$25) | $3.50 | $5.28 |
| DeepSeek V4 Pro ($0.435/$0.87) | $0.21 | $0.32 |
Acceptance Rate Changes Everything
If 80% of agent fixes are accepted, divide the average attempt cost by 0.8. If only 40% are accepted, divide by 0.4. A cheap model with low acceptance can become more expensive than a premium model with high acceptance. This is why teams should track fixed bugs, not just token spend.
The best workflow is usually tiered: cheap or midrange model for straightforward bugs, premium model for ambiguous production issues, and human intervention when the second repair loop fails. Use the AI Cost Estimator to compare model prices against the token shape of your real bug-fixing workflow.
Want to calculate exact costs for your project?
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