LLM Token Cost Calculator
Calculate your projected monthly LLM API token expenditure. Compare Claude 3.5 Sonnet, GPT-4o, and open-weights models based on daily query volume, average prompt token size, and response length.
Token Expenditure Parameters
Updated 2026-08-13Based on 30-day billing cycle (180M total input tokens & 52.5M output tokens per month).
Foundation Model API Pricing Reference Table
| Model Name | Input Price / Million Tokens | Output Price / Million Tokens | Context Window |
|---|---|---|---|
| Anthropic Claude 3.5 Sonnet | $3.00 | $15.00 | 200k tokens |
| OpenAI GPT-4o | $2.50 | $10.00 | 128k tokens |
| OpenAI GPT-4o Mini | $0.15 | $0.60 | 128k tokens |
| Anthropic Claude 3 Haiku | $0.25 | $1.25 | 200k tokens |
| Google Gemini 1.5 Pro | $3.50 | $10.50 | 2M tokens |
Optimizing Production Token Expenditure
Managing cloud LLM expenditures requires optimizing both prompt token size and completion token lengths. In Retrieval-Augmented Generation (RAG) applications, sending unnecessary vector chunks inflates input token costs by 400% without improving response precision.
Deploying prompt caching (e.g. Anthropic Prompt Caching or OpenAI Prefix Caching) reduces input costs on repetitive system prompts by up to 90%. For high-throughput background tasks, switching from proprietary cloud APIs to open-weights models (Llama 3 8B) served on dedicated vLLM instances yields massive cost savings at scale.
Frequently Asked Questions
How frequently is the LLM pricing table updated?↓
This table is maintained directly in a JSON configuration file and was last updated on 2026-08-13.
How do prompt tokens differ from output completion tokens?↓
Input tokens represent the prompt text, system instructions, and RAG context sent to the API. Output tokens represent the generated model completion text.