estimate_cost
Estimate monthly cost for LLM usage by providing input and output token counts, model or hardware, and customizing parameters like caching, batch mode, and electricity rate.
Instructions
Estimate monthly cost for LLM usage — API providers OR self-hosted hardware.
Args: input_tokens_per_month: total input tokens per month output_tokens_per_month: total output tokens per month model_or_hardware: specific model/hw ID (e.g. 'gpt-5', 'rtx-5090'). If None, returns full comparison table. amort_months: amortization period for hardware purchase, default 24 months electricity_eur_per_kwh: electricity rate, default 0.20 EUR/kWh (FR 2026 average) hours_active_per_day: hours of active inference per day, default 4 caching_hit_rate: 0..0.9 fraction of input tokens hitting prompt cache (Anthropic 90% off / Gemini 75% off). Default 0. batch_mode: True applies 50% off input+output for batchable APIs (24h delay). Default False. off_peak: True applies DeepSeek off-peak discount (50% V3.5 / 75% R1). Default False. pc_always_on: True = idle (24-hours_active) × idleW. False = PC off out of active hours (0W idle). Default True. vat_recoverable: True = subtract 16.67% VAT from hardware purchase price (HT pour pro). Default False.
Returns: cost estimate in EUR (with USD-to-EUR conversion ~0.92 for APIs)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| off_peak | No | ||
| batch_mode | No | ||
| amort_months | No | ||
| pc_always_on | No | ||
| vat_recoverable | No | ||
| caching_hit_rate | No | ||
| model_or_hardware | No | ||
| hours_active_per_day | No | ||
| input_tokens_per_month | Yes | ||
| electricity_eur_per_kwh | No | ||
| output_tokens_per_month | Yes |