Guide
What are input and output tokens? AI pricing explained simply
Short answer
Input tokens are what you send to the model. Output tokens are what the model writes back. Most API bills start there, then add details such as cached input, batch processing, tools, images, audio, or provider-specific features.
Target search intent: input output tokens explained.
Who should read this
Founders, operators, and non-developers who need to understand AI API bills before approving a project.
Decision framework
- Prompt size
- Answer size
- Cache behavior
- Batch processing
- Tool and media charges
Best-fit rule
Estimate cost from real task examples. Do not approve a budget from a single per-token number.
How to evaluate it in 30 minutes
- Open the official source pages below and confirm the current plan names, model names, pricing units, and limits.
- Write down the repeated job you actually need to complete. Avoid vague goals such as "use AI more."
- Test one realistic example from your own work, not a vendor demo prompt.
- Compare the result against a manual baseline: time saved, errors introduced, source quality, and review effort.
- Decide whether the tool or model should be adopted, watched, or ignored for now.
Simple scorecard
- Prompt size: score 1-5 after testing it against your own workflow.
- Answer size: score 1-5 after testing it against your own workflow.
- Cache behavior: score 1-5 after testing it against your own workflow.
- Batch processing: score 1-5 after testing it against your own workflow.
- Tool and media charges: score 1-5 after testing it against your own workflow.
Use the scorecard to make the decision explicit. A tool that scores high on one dimension but low on trust, export, or pricing clarity should stay in trial mode.
Recommended workflow
Ask for a 100-request sample with input tokens, output tokens, model name, and tool calls before launch.
What can go wrong
Tokens can look cheap until usage volume, long outputs, and tool calls are included.
FAQ
Can this page replace the official pricing or documentation page?
No. Use this page to understand the decision and the tradeoffs. Use the official source pages below for current prices, limits, model names, plan names, and availability.
When should I re-check this decision?
Re-check it before buying seats, approving a team rollout, changing a production model, or publishing a recommendation to clients. For pricing-heavy pages, a 2-4 week review cycle is safer than a quarterly review.
What is the fastest way to avoid a bad AI purchase?
Test the tool or model on one repeated workflow, score it with the framework above, and confirm the pricing unit before paying. If you cannot explain what is being billed, stay in trial mode.
How we verified
This brief was written from publicly available product pages, pricing pages, help centers, and developer documentation. Pricing, limits, plan names, and model access can change without much notice. Treat this as a decision guide and confirm the exact numbers on the vendor page before buying, migrating, or approving team spend.
Sources
Last verified: 2026-04-28.
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