Estimate the complete cost of one AI translation job by combining character-based model charges, optional human review labor, and fixed workflow overhead. The result separates machine usage from review expense and shows a blended cost per source word, making vendor comparisons and pricing decisions easier.
Total task cost = (source words × characters per word ÷ 1,000,000 × AI rate) + (review minutes ÷ 60 × hourly cost) + fixed overhead.
The main result is a planning estimate for one translation workload or its available capacity. It is only as accurate as the pricing, model-speed, token-density, and workload assumptions entered. Use measured production values whenever possible and run high- and low-case scenarios before committing capacity or a customer price.
This planning estimate does not guarantee vendor billing, model performance, transcription accuracy, translation quality, or service-level objectives.
For 1,200 words, 6 characters per word, $12 per million characters, 18 review minutes at $35/hour, and $0.15 overhead: AI = $0.09, review = $10.50, so total cost is $10.74.
Use representative production values or a weighted average from recent tasks. Peak values are better when testing capacity risk.
Not automatically. Increase workload, overhead, tokens, or utilization headroom to model retries and operational variance.
Yes. Enter each option’s measured speed, infrastructure assumptions, and labor or usage prices as separate scenarios.
Headroom helps absorb variable media length, traffic bursts, startup time, retries, and queueing without immediately missing targets.
Update it after pricing, models, hardware, workflow stages, average task size, or concurrency patterns change.
| Variable | Meaning |
|---|---|
| Workload | Media length, source volume, or concurrent demand entered above. |
| Efficiency | Measured model rate, token density, batching gain, or utilization assumption. |
| Overhead | Workflow work outside the primary model calculation. |
| Result | Estimated requirement or capacity before unmodeled operational variance. |