SourceVane · Practical AI
How retries change AI API cost per successful task
A reproducible scenario analysis separates token spend, average attempts and final successful tasks across four retry assumptions.
Direct answer
The decision in brief
Retries increase billed token spend in direct proportion to average attempts. A higher final success rate can still leave each accepted task more expensive.
Answer
Retry volume and success rate are separate inputs
Retries increase billed token spend in direct proportion to average attempts. A higher final success rate can still leave each accepted task more expensive.
Scenario results
| Scenario | Average attempts | Final success rate | Monthly token cost | Cost per successful task |
|---|---|---|---|---|
| No retries | 1.00 | 85% | $40.00 | $0.004706 |
| Limited retry queue | 1.10 | 90% | $44.00 | $0.004889 |
| Retry-heavy queue | 1.40 | 95% | $56.00 | $0.005895 |
| Two attempts on average | 2.00 | 99% | $80.00 | $0.008081 |
The success rates are scenario inputs. They are not model accuracy measurements.
Method and reproduction
For each row, monthly cost equals tasks × average attempts × token cost per attempt. Successful tasks equal tasks × the stated final success rate. Cost per successful task divides the first result by the second. The calculation uses six-decimal fixed-point inputs before display rounding.
- Keep the token count and price constant.
- Change average attempts and final success rate independently.
- Count tokens from failed attempts in the monthly total.
Limits
This is a scenario analysis, not a hands-on model benchmark. It excludes tool charges, cache pricing, hosting, taxes and differences in token use between successful and failed attempts. Replace every assumption with measured workload data before budgeting.
Revision and production notes
Study type: scenario analysis. Revision: 2026-09-13.1. Updated: 2026-09-13.
SourceVane publishes the assumptions, formulas and machine-readable rows so the result can be checked independently. Corrections change the revision and are recorded on the public page.
Continue this decision
Evidence and next steps
Reduce AI API costs without losing useful results
Measure workload, retries and accepted outputs before comparing models, batching or reusable prompts.
Measure AI API retry cost before changing models
Count failed attempts, final successful tasks and token use separately so retries do not hide the real cost of useful API work.
AI API pricing comparison
Compare selected first-party text API list prices using the same per-million-token unit, with conditions, verification dates and official sources.
AI API cost calculator
Compare two AI API budgets using token prices, retries and successful tasks. Enter your own assumptions; no API calls or account required.
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