OpenAI API costs for 1,000 workflows range from $0.90 to $56.25 depending on the selected model. For a standard workflow utilizing 1,500 input tokens and 500 output tokens, the GPT-5.6 Luna model provides the most economical path for high-volume automation.
Procurement and IT departments must differentiate between lightweight tasks and high-reasoning requirements. While entry-level models like Luna and Terra keep costs under $10 per 1,000 runs, specialized models like GPT-5.6 Cyber or GPT-Image-2 significantly increase the unit cost for more complex or multimodal operations.
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 |
$2.50 |
$15 |
$0.0112 |
$11.25 |
| gpt-realtime-2.1 |
$4 |
$24 |
$0.0180 |
$18 |
| gpt-image-2 |
$8 |
$30 |
$0.0270 |
$27 |
| gpt-5.6-cyber |
$12.50 |
$75 |
$0.0563 |
$56.25 |
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 cost per workflow?
Total spend is driven by the mix of input and output tokens. In the GPT-5.6 family, output tokens are priced significantly higher than input tokens—often by a factor of six. If a workflow generates long-form reports rather than short classifications, the cost per 1,000 workflows will increase even if the total token count remains the same.
Which model should be used for high-volume automation?
For everyday tasks, GPT-5.6 Luna offers a strategic advantage for Finance and IT teams looking to scale without exponential budget growth. Moving to models like GPT-5.6 Sol or Realtime-2.1 doubles or triples the cost, which is usually only justifiable for tasks requiring higher accuracy or lower latency than the baseline Luna model provides.
To determine if these API costs are a sound investment, teams must calculate the net return against labor hours recovered. If spending $0.90 on 1,000 Luna runs saves a department several hours of manual processing, the adoption is profitable. NetLift automates this tracking by connecting API spend to objective time-savings and evidence-based performance metrics, ensuring AI adoption delivers a measurable financial return.
Sources
All pricing on this page comes from official vendor pages: