How to Estimate AI Token Usage and Cost

AI cost planning has two moving parts: how a provider tokenizes the request and which billing rate applies to each token category. The same visible paragraph can produce different token counts across model families, especially when it contains Arabic, code, JSON, or uncommon symbols. Providers may price uncached input, cache reads, cache writes, output, and sometimes long-context tiers differently. A calculator is valuable when those assumptions remain visible instead of being compressed into one unexplained total.

Use the estimate for budgeting and model comparison, then use the API response's usage fields for reconciliation. Character-based estimation is a planning shortcut, not a tokenizer. Even a tokenizer result can differ from a complete request after system instructions, tool schemas, images, or provider-added formatting are included. Prices are live commercial data, so record the model name, rate date, currency, request count, and cache assumption beside any number that will inform a purchasing decision.

Choose the exact provider and model first. Count the prompt with a matching tokenizer or provider token-count endpoint when available; otherwise mark the character estimate as approximate. Divide input into uncached and cached-read tokens, enter expected output separately, and multiply the per-request calculation by the request count. Compare the combined tokens with the context window and the proposed output with the model's maximum. Finally, list omitted charges such as cache creation, storage, tools, images, batch discounts, or long-context premiums.

Every preset should be checked against the provider's current pricing and model documentation. Provider token-count endpoints or matching tokenizers support the count; response usage remains the authoritative record for a completed request.

Model a workload of three identical requests. Each request has 250,000 uncached input tokens, 750,000 cached-read tokens, and 20,000 output tokens. For every selected model, compute `0.25 × input price + 0.75 × cached-input price + 0.02 × output price`, then multiply by three. The input occupies one million tokens before output, so also compare 1,020,000 combined tokens with the documented context window. Save the result with the pricing date, then compare it with actual usage from a small production sample.

Estimated cost = requests × ((uncached input ÷ 1,000,000 × input rate) + (cached-read input ÷ 1,000,000 × cached rate) + (output ÷ 1,000,000 × output rate)). This formula is complete only for the categories represented by the chosen preset.

A zero cached-input value does not prove caching is unavailable; it only states that this scenario has no cache reads. Cache creation can cost more than ordinary input and storage can be billed by time. Some providers include hidden reasoning or thinking tokens in billable output, and tiered prices can change beyond a context threshold. Promotional rates, batch endpoints, regional taxes, currency conversion, minimum charges, and negotiated enterprise contracts can all move the final invoice away from the estimate.

Check the tokenizer status, context warning, pricing verification date, and provider source; use custom rates when the official or negotiated price differs. Watch for one recurring error: ignoring cached input and multiplying only one request when planning an agent, batch, or monthly workflow.

Select a provider preset or enter custom per-million rates, paste representative text or supply known token counts, split cached input from uncached input, add expected output and request volume, then review both cost and context-fit warnings.

Input: provider, model or custom rates, text or token totals, cache share, output allowance, and request count. Output: category-by-category estimated cost, token totals, and context-window status. No API call is made and no provider bill is retrieved.

Prompt text and selected local files are measured in the browser and no AI API call is made. OpenAI text uses a browser-loaded o200k tokenizer while other providers use a labeled character estimate; listed per-million input, cache, and output rates are then applied to the selected request volume.