Cost drivers that dominate token bills
Token cost is usually driven by three variables: input size, output size, and request count. In production workloads, output inflation is common because longer answers, tool traces, or safety text raise billed completion tokens. A practical baseline is to track medians, not averages, then re-run this calculator with a “p90” scenario. Many teams see overhead between 5–15% after accounting for retries, routing prompts, and formatting wrappers. Measure tokens per endpoint and per tenant to spot runaway features early and quantify the effect of prompt tuning.
Comparing model tiers for predictable budgeting
Pricing tiers matter most when your context grows. Long-context modes can raise input rates, but they may reduce failures and re-tries, which lowers total spend. Compare at least two tiers using the same token assumptions, then check the “USD / 1K tokens” column to normalize across request sizes. If two models are close, use the provider multiplier to reflect region or platform uplift.
Handling caching and cache writes correctly
Cache reads can materially reduce input costs for repeated system prompts, policies, or long reference snippets. Enter cached input tokens only for the portion you expect to be served from cache. Cache writes are billed differently by some providers, so this tool uses an input-rate multiplier to approximate write charges. If your stack does not bill cache writes, set the multiplier to 0.
Normalizing results with cost per 1K tokens
“Per call” totals are useful for budgets, but optimization work needs a normalized metric. Cost per 1K tokens helps you compare a short chat workflow against a long summarization workflow without mixing request counts. Use this number to set internal guardrails, for example, target under $0.01 per 1K for bulk classification, or accept higher spend for customer-facing reasoning where quality wins.
Operational reporting and chargeback workflows
Exporting CSV supports monthly spend reviews and variance checks. Save one row per model and attach assumptions (token sizes, discounts, exchange rate) to your ticket or finance memo. For chargeback, run the calculator per product feature, then apply margin and tax fields to match internal billing rules. Recalculate after prompt changes so governance stays aligned with real usage.
FAQs
What should I enter for input tokens?
Use the total prompt size per request, including system text, user content, tool schemas, and any retrieved context. If unsure, sample a few real calls and use the median.
Why are reasoning tokens billed as output?
Some providers bill hidden reasoning or “thinking” as completion tokens. Adding them to output keeps estimates conservative and prevents under-budgeting for complex tasks.
How do I estimate overhead percentage?
Start with 8–12% for stable apps. Increase it if you see frequent retries, tool calls, or long system wrappers. Reduce it after you validate production logs.
When do cached input tokens apply?
Use cached tokens only when your provider supports prompt caching and your workflow reuses identical content. Typical candidates are policies, long instructions, and static reference text.
How are discounts and margins applied?
Batch and volume discounts reduce the pre-modified cost, then margin and tax increase it. This ordering models common billing contracts while keeping each adjustment transparent.
Can I show totals in my local currency?
Yes. Choose a currency and enter the exchange rate for 1 USD to that currency. The calculator shows both USD and converted totals for each model.