Interactive · Week 2 · Sections 3, 5

Budget your context like the resource it is.

Most teams underprice their LLM bill by an order of magnitude — and most of them could cut it by 60–80% by enabling prompt caching alone. Drag the component sliders, pick a model, set a volume, and see what your real cost looks like with and without caching. The checkboxes mark which components are static enough to cache.

Context components

Total input tokens 0

Cacheable 0 (0%)

Settings

Provider · model

Volume 100/min

Requests per minute. Monthly = volume × 60 × 24 × 30.

Cache hit rate 90%

Fraction of requests that find a warm cache. Production teams typically see 75–95% once the prefix is stable.

Per request · no cache

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Per request · with cache

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Monthly · no cache

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at current volume

Monthly · with cache

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Per-request cost breakdown · with cache

What's happening here

Prices reflect approximate published rates as of early 2026 and round to two significant figures. Cache-write pricing follows the documented Anthropic model (1.25× input). OpenAI-style automatic caching is modeled at 50% discount with no write penalty. DeepSeek and Gemini approximated similarly. Real bills will vary; this calculator is for relative reasoning, not invoice prediction. Source-of-truth for the production version lives as <PromptCostCalculator/> in the curriculum codebase.