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Prompt compression savings calculator

Compare baseline prompt spend against shorter prompts plus cached context to quantify monthly savings.

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Prompt compression

Cost delta at volume

$391.6859.3% lower

4,800 input tokens saved / request · 20,000 requests

Baseline$660.00
No compression
Optimized$268.32
Shorter + cache
Tokens saved / req4,800
Token cut60%
Optimized / req$0.0134

Before / after

Compression inputs

Shorter prompts plus optional cache share on the compressed prefix.

Token bar

Before vs after tokens

Visualize the input cut that drives the cost delta.

Before8,000
After3,200
Token reduction60%
Cost savings59.3% · $391.68

Cache pricing applies only when the selected model lists a cached input rate. Cached share of compressed: 60%.

Cache pricing applies only when the selected model lists a cached input rate. Updated 2026-07-31. Approximate static list prices for planning only. Always verify against each provider’s official pricing page before production budgeting.

Guide

How to get value from this calculator

Estimate how much you save by shortening prompts, trimming context, or enabling prompt caching. Every removed token cuts input cost on every request, which compounds quickly at high monthly volume. Use this to justify compression, summarization, or cache investments with concrete dollar figures.

Cost formula

Savings = (original input tokens − compressed input tokens) / 1,000,000 × input price × monthly requests; With cache: savings += cached tokens / 1M × (full input price − cache read price) × hit rate × monthly requests

Why it matters

Long prompts are a recurring tax on every API call. Teams that grow context without measuring cost often spend more on redundant instructions and stale history than on the actual generation task.

How to use it

  1. Enter your current average input tokens per request including system prompt and context.
  2. Set a target reduced token count after compression or summarization.
  3. Read the before/after token bar for monthly savings from the reduction.
  4. Enter monthly request volume for the affected workload.
  5. Select the model and review monthly savings from token reduction alone.
  6. Optionally add cache hit rate and cache write/read pricing if using prompt caching.
  7. Compare savings against the engineering cost of implementing compression.

Planning tips

  • Summarize conversation history instead of sending full transcripts after 5–10 turns.
  • Move static instructions to a cached system block if your provider supports prompt caching.
  • Remove duplicate tool schemas and examples from prompts sent on every request.
  • Measure token count before and after compression in staging—aim for quality-neutral cuts.
  • Aim for quality-neutral cuts—the before/after bar shows dollar impact, not quality.
  • Output token pricing is unchanged by compression—focus on input-side reductions.

Frequently asked questions

Will compression hurt response quality?

Aggressive cuts can degrade results. This calculator shows cost impact only—you should eval quality after compression changes before deploying.

How does caching differ from compression?

Compression removes tokens from the prompt. Caching stores repeated prefix tokens so re-reads bill at a lower rate. Many production systems use both.

Are savings figures exact?

No. CentsPerToken uses approximate list prices for planning. Actual savings depend on real token counts, cache hit rates, and current provider rates.

What hit rate should I assume for caching?

Start with 50–70% for workloads with stable system prompts and shared document context. Measure actual hit rates in provider logs after deployment.

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