Running 1,000 workflows on the OpenAI API costs $0.40, based on an assumption of 1,500 input tokens and 500 output tokens per run.
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At a standard volume of 1,500 input tokens and 500 output tokens per run, the OpenAI API costs $0.40 per 1,000 workflows. This equates to a unit price of $0.0004 per individual workflow execution.
Procurement and IT teams use these figures to project monthly spend as agentic workloads scale. This pricing model charges $0.1 per 1 million input tokens and $0.5 per 1 million output tokens.
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 |
|---|---|---|---|---|
| OpenAI API | $0.1 | $0.5 | $0.0004 | $0.4 |
Cost per workflow = (input tokens x input price + output tokens x output price) ÷ 1,000,000, using verified official per-token prices.
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The total expense scales linearly based on the length of prompts and generated responses. While the benchmark of $0.40 per 1,000 runs provides a baseline, actual costs fluctuate if your workflows require larger contexts or complex reasoning outputs. Finance teams should note that output tokens are five times more expensive than input tokens.
When building budget guardrails, teams must account for the specific token ratios of their use cases. Since the OpenAI API charges $0.1 per 1M input and $0.5 per 1M output, workflows that are "output-heavy" will reach budget limits faster than those with large inputs and concise summaries.
To determine if $0.40 per 1,000 workflows represents a positive return, teams must track the time saved against the manual labor baseline. NetLift measures this net return by comparing API consumption costs against the realized efficiency gains, providing the evidence quality required for continued AI investment.
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All pricing on this page comes from official vendor pages:
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About the author
Paige Gilmore · Founder, NetLift
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption.
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