How this calculation works
The API & LLM Token Cost Calculator estimates monthly and annual cloud API expenditures for AI models (OpenAI, Anthropic, Gemini) and microservices based on token consumption and request throughput.
Mathematical formula and logic
Monthly Cost = Requests × [(Input Tokens × Input Rate / 1M) + (Output Tokens × Output Rate / 1M)].
Worked example
100,000 monthly requests with 800 input tokens ($2.50/M) and 250 output tokens ($10/M): Input cost = $200, Output cost = $250. Total monthly API cost = $450 ($5,400/year).
Calculation assumptions
- Token volume is uniform across requests.
- Base pricing follows standard million-token tiered billing.
Frequently asked questions
How many words is 1,000 tokens?
In English, 1,000 tokens corresponds to approximately 750 words (about 3/4 of a word per token).
Why are output tokens more expensive than input tokens?
Generating output tokens requires continuous sequential autoregressive GPU computation, whereas input tokens are processed in parallel during prompt caching.
How can I reduce LLM API costs?
Use prompt caching for system messages, implement shorter system prompts, switch to smaller distillation models (like GPT-4o-mini or Gemini 1.5 Flash), and batch repetitive requests.
What is context window caching?
Prompt caching stores parsed prefix tokens on the provider's server, reducing latency and slashing token input costs by up to 50% to 80% on repeat calls.
How do API costs scale with user growth?
API costs scale linearly with active users unless mitigated by client-side local caching, semantic search vector filtering, and rate limiting.