Prompt Cost Analyzer
Estimate the monthly cost of a prompt across OpenAI's GPT-5 models — and see how prompt caching changes the math at different traffic patterns.
Cost shape
Cache only discounts the system sliceSystem is only 3.7% of cost — caching can't move the bill much in this shape. Either increase the system prompt or decrease output tokens to see meaningful cache savings.
Monthly cost
Pick the column that matches your traffic pattern. No cache = prompt too short to engage, or prefix changes per call. Bursty ≈ 50% hit (intermittent traffic with gaps wider than the cache TTL). Steady ≈ 80% hit (regular production traffic, occasional gaps). Always-warm ≈ 95% hit (sustained traffic, byte-stable prefix).
| Model | Tokens In / Out | $ / Call | No cache | Bursty | Steady | Always-warm |
|---|---|---|---|---|---|---|
GPT-5.5 in $5 · out $30 · cache $0.50 | 60 → 200 | $0.0063 | $630 | $619 −1.7% | $613 −2.7% | $610 −3.2% |
GPT-5.4 in $2.50 · out $15 · cache $0.25 | 60 → 200 | $0.00315 | $315 | $310 −1.7% | $307 −2.7% | $305 −3.2% |
GPT-5.4 mini in $0.75 · out $4.50 · cache $0.075 | 60 → 200 | $0.000945 | $94.50 | $92.91 −1.7% | $91.96 −2.7% | $91.49 −3.2% |
GPT-5.4 nano in $0.20 · out $1.25 · cache $0.02 | 60 → 200 | $0.000262 | $26.20 | $25.78 −1.6% | $25.52 −2.6% | $25.40 −3.1% |
How caching works here
This is a single-turn model — system prompt is treated as the cacheable prefix; user prompt and output tokens bill at standard rates. In a multi-turn conversation or agent loop, the cacheable prefix actually grows to include prior turns, so cache savings scale further than what's shown here. On a cache hit, prefix tokens bill at OpenAI's discounted cache-read rate — about 10% of standard input pricing for the GPT-5 family. Real applications with long, stable prefixes and high call volume often sustain 80%+ hit rates, turning the cache columns into the actual P&L line.
Token counts are approximate (±10%). Estimated from character length; actual counts will differ slightly by content.
Source · verified 2026-05-12
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