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LLM Batch Processing Cost per Task Calculator

The LLM Batch Processing Cost per Task Calculator estimates unit cost for a batch job by combining separately priced input and output tokens with a fixed job-level overhead allocated across all tasks. It also reports the expected total job cost and cost per thousand tasks.

This model is useful for backfills, periodic generation, evaluation pipelines, and other workloads that may receive batch-specific pricing. Enter the rates that actually apply to the job; discounts, storage charges, orchestration, validation, and failed-record handling vary by provider and architecture. Allocating overhead evenly is most appropriate when tasks are similar. For mixed workloads, separate task groups or use weighted token averages.

Batch pricing assumptions

tokens
tokens
USD
USD
USD
tasks
Result
—
estimated cost per batch task
Token cost per task—
Allocated job overhead per task—
Estimated total job cost—
Cost per 1,000 tasks—

1. Enter token averages
Provide expected input and output tokens for one batch task.

2. Set batch prices
Use the applicable price per one million tokens for each token class.

3. Add job overhead
Include fixed costs incurred once per submitted job, such as staging, validation, or orchestration.

4. Enter tasks per job
The fixed overhead is divided evenly across this count.

5. Review unit cost
The main result combines token cost and allocated overhead.

6. Check scale economics
Compare total job cost and cost per 1,000 tasks at different batch sizes.

Token cost per task = Input tokens ÷ 1,000,000 × Input price + Output tokens ÷ 1,000,000 × Output price Overhead per task = Job overhead ÷ Tasks per job Cost per task = Token cost per task + Overhead per task

What the result means

The main result is the expected average cost assigned to each task in the batch.

Estimates depend on the model, serving stack, hardware, quantization, batching strategy, and workload mix. Validate the result with measurements from your own environment before committing capacity or budget.

Given

  • 1,000 input tokens per task
  • 220 output tokens per task
  • $0.75 per 1M input tokens
  • $3.00 per 1M output tokens
  • $45 job overhead
  • 500,000 tasks

Calculation

Token cost = 1,000 ÷ 1,000,000 × $0.75 + 220 ÷ 1,000,000 × $3.00 = $0.00141. Overhead share = $45 ÷ 500,000 = $0.00009. Total = $0.00150 per task.

Result

$0.001500 per task and $750.00 for the full job.

The fixed overhead contributes six percent of the unit cost in this example.

Does a larger batch always reduce cost per task?

It reduces the allocated fixed overhead per task, but token cost remains unchanged unless pricing or token usage also changes.

Where should failed-record cost be included?

Increase average token usage, add a reprocessing factor before using this calculator, or include expected failure expense in job overhead.

Can storage and data transfer be included?

Yes. Add fixed job-level amounts to overhead, or convert variable charges into an expected per-task amount and add them to the token-based unit cost externally.

What if input and output use different discount rules?

Enter the effective price for each token class separately. Do not apply one blended discount unless it accurately represents both.

How is this different from the inference cost calculator?

This version allocates a job-level overhead across many deferred tasks and is designed for batch pricing. Interactive inference often has different prices and reliability adjustments.