ContextJet-ai Estimate LLM cost
Cost surprises come from not doing the arithmetic. This skill ships a small, dependency-free calculator so you can price a call, project a monthly bill, and compare models with actual numbers.
Use the bundled script
scripts/llm_cost.py is pure Python, no install needed:
from llm_cost import estimate_cost, project_monthly
estimate_cost(1500, 300, model="gpt-4o") # one call, USD
project_monthly(1500, 300, calls_per_day=5000, model="gpt-4o") # monthly projection
estimate_cost(2000, 200, model="gpt-4o", cached_input_tokens=1800) # with prompt caching
Run it directly to see a worked example: python scripts/llm_cost.py.
Prices in the PRICES table are approximate and change often, so pass input_price/output_price explicitly when you need exact figures, or edit the table. The arithmetic (not the price table) is what the tests pin down.
How to apply it
- Price the call with realistic token counts (measure them from a trace, see
instrument-llm-observability). - Project the bill with your real call volume. A cheap call at 10k/day beats an expensive one at 10/day.
- Compare models by running the same tokens through two model ids. Pick the cheapest that still passes your evals (see
compare-llm-models). - Feed it into cost cutting (see
reduce-llm-cost).
Validation
Run the tests: pytest skills/estimate-llm-cost/tests/. They check the math is exact for explicit prices, that model lookups match the table, that prompt-cache discounting is correct, and that monthly projection scales linearly.
Anti-patterns
- Trusting the built-in price table as current (verify against provider pricing).
- Pricing one call and forgetting to multiply by real volume.
- Comparing models on price without checking quality holds on your eval set.