OpenAI API Pricing: A Practical Planning Guide

OpenAI charges separately for the tokens you send (input) and the tokens the model generates (output), with output almost always priced higher per token. Some models also expose a discounted rate for cached input — repeated prompt prefixes such as a long system prompt or reference document. Because rates differ per model and change over time, we do not reprint them here; confirm the current numbers on the official pricing page linked above.

OpenAI publishes a range of models, from smaller, cheaper models aimed at high-volume simple work to larger flagship and reasoning-oriented models for harder tasks. There is no universal "best" model — the right choice is use-case-dependent. A useful starting rule of thumb:

• High-volume, well-defined tasks (classification, extraction, short replies): start with a smaller model and only upgrade if quality is insufficient.
• General assistant and content work: a mid-tier model usually balances quality and cost.
• Complex reasoning, code, multi-step planning: a flagship or reasoning model may earn its higher price by reducing retries and human corrections.

Because a smaller model can be several times cheaper per token, testing whether it meets your quality bar is often the single biggest cost decision you will make.

• Prompt size: trimming a bloated system prompt or retrieving fewer, more relevant documents directly cuts input tokens on every request.
• Output limits: capping response length with a max-output setting prevents runaway generations, which matter most because output is the pricier side.
• Cached input: if you resend the same prefix constantly, caching can reduce the cost of that repeated portion.
• Batch mode: for work that tolerates delayed, asynchronous results, OpenAI's batch API is typically offered at a discount versus real-time calls.
• Model routing: send easy requests to a cheap model and escalate only hard ones to a flagship model.

That is roughly 72M input and 15M output tokens per month. The planning takeaway is not a dollar figure — it is the shape of the problem: input dominates token volume here, so the highest-leverage optimisations are shrinking the retrieved context and using cached input for the static system prompt. Enter your own numbers in the AI API Cost Calculator to convert this shape into a monthly range with current rates.

Prototype with realistic prompts, measure the actual input/output token counts your app produces, then multiply by current rates. Build a range (low/expected/high) rather than a single figure, because prompt growth and traffic spikes are normal. Always verify rates on OpenAI's official pricing page before committing a budget.