OpenAI Cost Per Token (Practical Guide)
Understand OpenAI cost drivers and how to estimate cost per token for input and output.
Most “OpenAI cost per token” confusion comes from mixing pricing units, token types, and billed retries.
What “cost per token” really means
OpenAI pricing is typically split into input and output token rates. Your bill scales with both.
Cost breakdown
- Input tokens: prompt + tool context
- Output tokens: model response
- Extra calls: retries and tool chains multiply total tokens
Example estimate
If your workflow uses 10k input tokens and 3k output tokens per request, multiply by the input/output rates and then by your request count.
Common mistakes
- Ignoring output tokens
- Assuming one model call equals one user action
- Not accounting for retries/tool amplification
Next step
Use the calculator pages to turn token estimates into daily/monthly cost.
Related reading
A clear way to estimate Claude costs, including input vs output tokens and workflow call volume.
How to estimate DeepSeek costs and compare value across tasks, especially when you optimize prompts and reduce retries.
A decision framework for choosing between GPT-4 and Claude based on real workflow token usage.
Understand Kimi pricing with input/output tokens and workflow call volume so you can control spend.