LLM API Cost Calculator
Estimate LLM API cost projections. Select models and volume parameters to calculate cost per request, day, month, and year.
Projected Expenses
Monthly Cost Comparison Across Models
Compare what it would cost to run your configured volume of 1,000 requests/day (1,000 in / 500 out) across other models.
Frequently Asked Questions
How do input and output token costs differ?
Inference APIs charge significantly more for output tokens than input tokens (typically 4x to 5x higher). This is because generating tokens requires maintaining the model state sequentially, which is far more GPU-compute intensive than processing inputs in parallel.
What is context caching and how does it save costs?
Providers like Google and DeepSeek offer context caching. If you reuse large static blocks of text (like system prompts or RAG context) across subsequent requests, you only pay a fraction of the cost (up to 90% cheaper) for cache-hits, drastically reducing token bills.
Why should I use a local LLM instead of APIs?
If your system processes millions of requests a day, API costs grow linearly. Self-hosting open-weight models (like Llama 3) converts variable token costs into fixed hardware amortization expenses, which becomes highly profitable at massive query volumes.
How LLM Pricing Schemes Work
Commercial LLM providers charge on a utility billing structure based on the quantity of million tokens processed (MTok). This pricing is split into two components:
- Input Tokens: Prompt tokens sent to the API, including system instructions, context retrievals, and history.
- Output Tokens: Completion tokens generated by the LLM response.
Factors Affecting API Cost Scales
API pricing is heavily linked to model scale. Lightweight models (e.g., GPT-4o mini or Gemini 1.5 Flash) cost fractions of a cent per request and are ideal for high-frequency classification tasks. Large, highly reasoning frontier models (e.g., Claude 3.5 Sonnet or GPT-4o) cost up to 20x more but are required for multi-step reasoning, agent execution, and complex coding assignments.
How to Reduce API and Token Costs
Developers optimize costs by:
- Prompt Pruning: Removing boilerplate system roles and restricting instructions.
- Dynamic Routing: Routing simple intents to mini models and reserving advanced reasoning for frontier models.
- Cache Optimization: Structuring requests to take advantage of prompt cache-hits.
- Output Truncation: Limiting max output tokens to prevent long conversational loops.