The cost to execute 1,000 workflows via the OpenAI API ranges from $0.90 for high-efficiency models like gpt-5.6-luna to $80.00 for gpt-realtime-2.1. These figures assume a standard workflow size of 2,000 total tokens (1,500 input and 500 output).
Finance and IT leaders must match the model tier to the task complexity to maintain budget control. While entry-level models provide extreme cost efficiency for simple logic, moving to higher-tier models like gpt-6-astra increases the unit cost of automation to $32.50 per 1,000 runs.
What OpenAI 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 |
| gpt-5.6-luna |
$0.2 |
$1.20 |
$0.0009 |
$0.9 |
| gpt-5.6-terra |
$2 |
$12 |
$0.0090 |
$9 |
| gpt-5.6-sol |
$4 |
$20 |
$0.0160 |
$16 |
| gpt-6-astra |
$5 |
$50 |
$0.0325 |
$32.50 |
| gpt-realtime-2.1 |
$32 |
$64 |
$0.0800 |
$80 |
Cost per workflow = (input tokens x input price + output tokens x output price) ÷ 1,000,000, using verified official per-token prices.
How does model selection impact the unit cost?
The spread between model tiers is significant, with a nearly 90x price difference between the most and least expensive options. For example, gpt-5.6-terra costs $9 per 1,000 workflows, while the more advanced gpt-5.6-sol costs $16. Choosing the right model involves balancing the necessity of advanced reasoning against the lower overhead of the Luna or Terra tiers.
What variables should Procurement monitor?
Total cost is driven by the volume of tokens processed per run. If your specific workflow requires more than the assumed 1,500 input or 500 output tokens, the cost per 1,000 runs will scale linearly. IT and Procurement teams should audit token density for specific use cases—such as data extraction versus creative synthesis—to ensure budget forecasts remain accurate as adoption scales.
To determine if this API spend is a viable investment, organizations must measure the time saved per workflow against the hardware and token costs. NetLift helps finance teams quantify this net return by comparing actual API consumption data against baseline labor costs, providing clear evidence of whether a model like gpt-6-astra pays for itself in productivity gains.
Sources
All pricing on this page comes from official vendor pages: