Estimate your GPT-4o API usage cost based on input tokens, output tokens, and optional cached input tokens. This calculator helps you quickly project per-request and total spend for apps, agents, and production workloads.
A GPT-4o cost calculator estimates API spend by combining token usage with the model’s pricing for input, output, and sometimes cached input tokens. Since LLM billing is usually based on token counts rather than per-call flat fees, even small changes in prompt length or response size can noticeably affect your total cost at scale. This type of calculator is useful for teams building chatbots, AI assistants, content tools, and internal automations. By modeling cost per request and multiplying it by expected traffic, you can forecast monthly budgets, compare prompt strategies, and decide whether optimizations like prompt compression or caching are worth implementing. The most accurate way to use a calculator is to enter your real average token counts from logs or API usage reports. If pricing changes over time, keeping the token rates editable makes the tool more flexible and helps you update estimates without rebuilding the page.
GPT-4o API cost is typically calculated by multiplying input, output, and cached input token counts by their respective per-million-token rates, then summing the results. If you want a total campaign or monthly estimate, multiply the per-request cost by the number of requests.
Cached input tokens are prompt tokens that may be billed at a lower rate when reused through supported caching mechanisms. They can reduce costs for repeated context, system prompts, or recurring conversation history depending on the API setup.
Model pricing can change, and different plans or providers may use different rates. Editable token prices let you keep the calculator accurate and adapt it for updated GPT-4o pricing or custom internal estimates.
If you use the Responses API or long system prompts, check whether your traffic gets cached-input pricing before trusting this calculator. In 2026, many teams overestimate GPT-4o costs because they model every repeated prefix at full input-token price. For workloads with stable instructions, schemas, tools, or conversation headers, cached tokens can materially cut spend. The opposite mistake also happens: assuming cache savings on highly personalized prompts. Run estimates with and without cache-hit rates, and document the assumed hit percentage in your budget.