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Estimate AI inference costs from the workload you expect to run—not from a model name alone. Count input and output tokens for representative requests, apply the current rates for the model and service tier, then add applicable charges for caching, tools, or other modalities. State the assumptions and the date you checked prices; the result is a forecast, not a guaranteed bill.
Start with representative requests
First define what your application will actually send and receive. A request’s input can include system instructions, conversation history, retrieved documents, and the latest user message. Its output can include more than the visible answer if the provider bills other generated tokens—such as reasoning tokens—as output.
Use provider usage data or a representative sample of requests to estimate token counts when available. If you do not have usage data yet, describe low, expected, and high scenarios and document how you chose the counts. Avoid treating one short prompt as representative if production requests will include long histories, retrieval results, or multi-step agent activity.
- Record expected input and output tokens for each type of request.
- Include conversation history and system instructions in the input estimate.
- Account for repeated calls, retries, agent loops, and other steps that can multiply usage.
- Separate materially different request types rather than relying on one blended average.
Calculate the token subtotal
When a service publishes separate input and output rates per million tokens, calculate each part separately:
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Estimated token cost = (input tokens ÷ 1,000,000 × input price per million) + (output tokens ÷ 1,000,000 × output price per million)
For recurring usage, multiply the representative request cost by the expected monthly number of requests:
Estimated monthly token cost = requests per month × estimated cost per representative request
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If your workload has several request classes, calculate each class separately and add the class totals:
Estimated monthly token cost = Σ (monthly requests in class × estimated cost per request in that class)
These are planning formulas, not a provider-issued bill guarantee. Do not apply one blended token rate unless you calculate that rate from the workload’s actual input/output mix and any applicable tiers.
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Worked example with hypothetical rates
Suppose a hypothetical rate card charges $2 per million input tokens and $8 per million output tokens. A request using 4,000 input tokens and generating 1,000 output tokens would have this token subtotal:
(4,000 ÷ 1,000,000 × $2) + (1,000 ÷ 1,000,000 × $8) = $0.016
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Check every pricing dimension that applies
A public per-token rate is only one part of the estimate. Check the selected model’s current rate card and billing definitions for the service path you intend to use. OpenAI’s pricing page lists rates per one million tokens and separates input, cached input, cache writes, and output, including short- and long-context rates for listed models (OpenAI API pricing). Google’s Gemini API pricing separates input, output, and context caching for listed models, states that output pricing includes thinking tokens, and lists separate prices for some grounded requests (Google Gemini API pricing). Anthropic’s list-price document dated May 27, 2026 distinguishes standard and batch processing and includes cache-write and cache-hit rates, with scope and context-window details (Anthropic list prices dated May 27, 2026).
Input, output, and reasoning
Apply the published input rate to billable input tokens and the output rate to billable generated tokens. Check the provider’s billing definition for reasoning or thinking tokens: a short user-visible answer does not necessarily mean the service generated only a short billable output.
Context length
Check whether the model has different pricing at a long-context threshold and whether your requests cross it. A rate for a shorter context may not apply to a request near or beyond that threshold. Use the rate tier that matches the actual request and the provider’s definition of context length.
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Prompt caching and cache storage
Do not assume that repeated-looking text will be discounted. Estimate the share of tokens that are eligible for and actually receive cache hits, and include cache creation or storage costs where the provider charges them. OpenAI’s pricing page lists distinct cache-related rates; Google’s optimization documentation explains that explicit cache objects have a time-to-live and are billed based on cached token count and storage duration (Google Gemini API optimization documentation). Use the current eligibility and billing rules for your selected model.
Batch or other processing tiers
If you plan to use batch processing or another service tier, use its rate only for the traffic that will actually use that mode and meet its conditions. Keep standard and batch traffic separate in the estimate when they have different rates.
Tools, grounding, and modalities
Search grounding, code execution, images, audio, video, and other non-text inputs can add charges or use different accounting. Add those items separately rather than treating a text-token subtotal as the complete bill. Consult the selected service’s rate card for the applicable tool or modality charges.
Region and serving channel
Where pricing varies by region, cloud platform, or endpoint, use the rate for the deployment you will run. A comparison between providers or models is meaningful only when it reflects the intended serving channel and the same workload.
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- Define request classes. Separate workload types with meaningfully different prompts, context lengths, outputs, or tool use.
- Estimate tokens for each class. Use observed provider usage when possible; otherwise document low, expected, and high assumptions.
- Choose the deployment and pricing tier. Confirm the model, region or serving channel, context tier, and processing mode against the provider’s current rate card.
- Apply the relevant token rates. Calculate input and output separately, then account for eligible cached tokens and cache writes using the provider’s billing rules.
- Add non-token charges. Include applicable tool, grounding, modality, storage, or other charges not covered by the token subtotal.
- Scale by expected monthly volume. Multiply each request class’s cost by its expected monthly count and sum the results.
- Compare the forecast with observed usage. After representative traffic runs, check provider usage records or invoices and adjust the assumptions that differ from actual traffic.
Keep the model, rate source, price-check date, token assumptions, request volumes, and excluded charges with the forecast. Provider prices and billing definitions can change, and the official pricing pages do not all display an explicit publication date. Recheck the relevant rate card before relying on a saved estimate.
Compare options on the same workload
A lower listed input rate does not by itself mean a lower total bill or equivalent task quality. Compare options using the same request classes and token mix, and account for differences in model capability appropriate to the task, long-context behavior, cache and batch eligibility, region or serving channel, and extra tool or modality charges. The reviewed price schedules are examples of billing dimensions, not a complete market survey or a recommendation of one model over another.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
