Running 1,000 workflows on Anthropic’s models costs between $0.27 and $12.00 depending on the model tier selected. These calculations are based on a standard assumption of 1,500 input tokens and 500 output tokens per execution.
For IT and Finance leaders, the price gap between the standard Claude API and Claude 3.5 Sonnet represents a significant variance in operational spend. Selecting the right model requires balancing the cost per thousand runs against the specific reasoning requirements of the task.
What Anthropic API costs per 1,000 workflows
Assumption (stated, adjustable): each workflow run uses 1,500 input tokens and 500 output tokens.
| Model |
Input $/1M |
Output $/1M |
Cost per workflow |
Cost per 1,000 workflows |
| Claude API |
$0.1 |
$0.5 |
$0.0004 |
$0.4 |
| Claude Platform |
$0.1 |
$0.5 |
$0.0004 |
$0.4 |
| Anthropic Claude 3.5 Sonnet v2 |
$0.6 |
$15 |
$0.0084 |
$8.40 |
| Anthropic Claude 3.5 Sonnet |
$3 |
$15 |
$0.0120 |
$12 |
Cost per workflow = (input tokens x input price + output tokens x output price) ÷ 1,000,000, using verified official per-token prices.
How does token distribution affect the total cost?
Output tokens are priced significantly higher than input tokens across all models. For Claude 3.5 Sonnet, output tokens are five times more expensive than input tokens per million. Organizations running long-form generation tasks will see higher costs per workflow than those using the API for short classifications or data extraction.
What should procurement consider for model selection?
The standard Claude API offers a much lower entry point for high-volume, low-complexity workflows. In contrast, Claude 3.5 Sonnet is priced for more advanced use cases. When forecasting monthly budgets, Procurement should evaluate if the increased cost of Sonnet provides a measurable improvement in workflow accuracy to justify the $11.73 premium per 1,000 runs.
Measuring the net return of Anthropic spend
To determine if the API spend is generating value, teams must compare the $12.00 per 1,000 workflows cost against the manual labor baseline it replaces. If the model saves two minutes of human review per run, the return on investment is substantial. NetLift provides the framework to track these unit costs alongside time-saved metrics, ensuring AI adoption moves beyond experimentation into measurable financial gain.
Sources
All pricing on this page comes from official vendor pages: