OpenAI API Cost per 1,000 Workflows
By Paige Gilmore, Founder, NetLift · Published 2026-07-28 · Updated 2026-08-11
Prices checked 2026-08-16 against official vendor pricing pages.
For a standard workflow using 1,500 input and 500 output tokens, 1,000 runs cost between $0.90 on GPT-5.6 Luna and $135 on GPT-5.5 Pro.
Running 1,000 standard workflows on the OpenAI API costs between $0.90 and $135, depending on the model selected. These estimates are based on a typical execution profile of 1,500 input tokens and 500 output tokens per run.
Models such as GPT-5.6 Luna and GPT-5.4 Nano offer the most economical entry point at less than $1.00 per thousand workflows. High-performance models like GPT-5.5 Pro represent the ceiling of the current pricing structure, designed for tasks requiring significant reasoning and complex outputs.
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 do token volumes affect total workflow spend?
Total expenditure is determined by the volume of data processed. In this 2,000-token scenario, input tokens account for 75% of the volume, but output tokens are priced at a higher rate per million. For example, GPT-5.6 Sol charges $30 per 1M output tokens compared to $0.5 for input, making the output efficiency a primary driver of the $15.75 cost per 1,000 workflows.
Which models offer the best price-performance for scaling?
Procurement teams looking to scale high-volume automation often evaluate GPT-5.6 Luna or GPT-5.4 Mini. While Luna provides a low-cost baseline of $0.90 per 1,000 runs, GPT-5.4 Mini offers a middle-ground alternative at $3.38. Choosing between these and premium tiers like GPT-5.5 ($22.50) depends on whether the specific workflow requires the advanced capabilities of the more expensive models.
Measuring the return on AI spend requires comparing these API costs against the human labor hours saved per workflow. At NetLift, we help finance and IT leaders verify if a $0.90 or $135 investment per thousand runs delivers a positive net return by analyzing time-savings against the total cost of ownership and model performance quality.
Sources
All pricing on this page comes from official vendor pages:
- https://platform.openai.com/docs/pricing — retrieved 2026-08-11
Frequently asked questions
Is GPT-5.6 Luna a game changer for high-volume API projects?
With a cost of only $0.90 per 1,000 workflows, GPT-5.6 Luna is the most cost-effective option for developers. This low price point supports high-frequency tasks that might be cost-prohibitive on more expensive models like GPT-5.5 Pro.
How does the cost of GPT-5.4 Mini compare to the Pro models?
GPT-5.4 Mini costs $3.38 per 1,000 workflows. This is significantly more affordable than GPT-5.5 Pro, which costs $135 for the same volume of work, representing a 40x price difference between the entry-tier and premium reasoning models.
What are the costs for real-time and high-reasoning models?
For 1,000 workflows, GPT-Realtime-2.1 costs $18.00, while GPT-5.5 costs $22.50. These models are priced higher than the Nano or Luna versions to account for specialized performance requirements.
Does GPT-5.6 Terra offer better value than standard GPT-5.4?
GPT-5.6 Terra costs $9.00 per 1,000 workflows, while GPT-5.4 is priced at $11.25. For organizations running large-scale operations, opting for the Terra model provides a 20% cost reduction per thousand runs.
About the author
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption. Paige Gilmore on LinkedIn