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LLM Inference Cost per Task Calculator

Estimate unit cost for LLM online inference using workload inputs you can replace with measurements from your own model and serving environment. The calculator shows the primary planning result together with supporting capacity or cost figures, so you can compare demand, operating assumptions, and available resources. It runs entirely in your browser and does not send workload data to a server.

Calculator

Workload assumptions

How to use this calculator

Replace the defaults with measurements for your workload.
Keep units consistent with each field label.
Select Calculate and review the main result plus supporting figures.
Repeat with a peak or conservative scenario before committing capacity.

Formula

Cost/task = input tokens × input rate ÷ 1M + output tokens × output rate ÷ 1M + cached tokens × cached rate ÷ 1M + other cost.

Every rate is applied in the unit shown beside its input. Values are calculated without intermediate rounding; displayed results are rounded for readability.

What the result means

The main result is a planning estimate of unit cost for the stated online inference assumptions. Supporting values expose the capacity, reserve, time, or cost components behind that estimate.

This calculator is an engineering estimate, not a guarantee. Benchmark the exact model, hardware, provider, and prompt distribution before production sizing or procurement.

Example calculation

With 1,200 input tokens at $1/M, 300 output tokens at $4/M, no cached tokens, and $0.002 overhead, cost is $0.0044 per task.

Tips for better results

Measure with representative prompt lengths and output limits.
Separate average and peak scenarios instead of relying on one blended case.
Include retries, failures, and operational reserve where relevant.
Recalculate after changing model, quantization, hardware, or serving software.
Keep the raw benchmark and its test conditions with your capacity plan.

Frequently asked questions

Which measurements should I use for llm inference cost per task calculator?

Use observed averages from the same model, hardware, prompt mix, and serving configuration whenever possible. Planning inputs are only as reliable as the measurements supplied.

Does the estimate include queueing and startup overhead?

Only overhead represented by the visible inputs is included. Add a conservative allowance or use measured end-to-end values when queueing, model loading, storage, or orchestration is material.

Can I use average values when workloads vary widely?

Yes for an initial budget, but also test separate high-volume and long-context scenarios because averages can hide peak resource demand.

Why does the calculator avoid a fixed industry benchmark?

LLM performance and pricing depend on model size, hardware, precision, batching, provider, and prompt mix, so a universal benchmark would be misleading.

How should I validate this planning result?

Run a representative load test, compare observed totals with the estimate, and update the inputs before making a production capacity or purchasing decision.

Related calculators

LLM Inference Token Budget Calculator LLM Inference Latency Capacity Calculator LLM Inference GPU Requirement Calculator LLM Inference Throughput Calculator

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Input guide

Variable	How to use it
Input tokens per task	Uncached prompt and context tokens.
Output tokens per task	Generated completion tokens.
Cached input tokens	Input tokens billed at a separate cached rate.
Input price per million	Rate for uncached input.
Output price per million	Rate for generated output.
Cached input price per million	Set to zero when cached tokens are free or unused.
Other cost per task	Infrastructure, retrieval, or orchestration cost.