AI API Cost & Token Calculator
Forecast your monthly LLM expenses and compare inference costs across Anthropic Claude 3.7, OpenAI GPT-4o, Google Gemini 2.5, DeepSeek, and Groq. All math runs locally in your browser.
⚙️ Operational Workload
📊 Comparative Monthly Spend by Model
📋 Granular Rate & Run-Rate Breakdown
| Model & Provider | Input Rate (1M) | Output Rate (1M) | Cost / 1k Reqs | Monthly Spend | Annual Run-Rate |
|---|
Output tokens cost 3x to 5x more than input tokens. To slash inference bills by 70%+, enforce strict output length limits in system prompts, route simple categorization to economy models (Gemini Flash or GPT-4o mini), and architect system prompts to leverage static prefix caching.
How AI Model Pricing Works in 2026
Large language models do not bill on execution duration or CPU runtime; they bill strictly by the token volume processed. In standard English prose, 1,000 tokens corresponds to roughly 750 words (approximately 1.5 single-spaced pages).
1. The Asymmetric Pricing of Input vs. Output
Notice in the calculator that output tokens (generations) are billed at a significant premium compared to input tokens (prompts). For instance, Anthropic charges $3.00 per million input tokens for Claude 3.7 Sonnet, but $15.00 per million output tokens (a 5x multiple). Generating verbose, conversational pleasantries directly burns your operational budget.
2. Prompt Caching: The 90% Margin Multiplier
Modern frontier models (Anthropic Claude, OpenAI GPT-4o, and Google Gemini) feature prompt caching. When multiple API requests share an identical starting block of text (such as system instructions, tool declarations, or static legal context), the inference engine caches the key-value (KV) attention states in memory:
- Anthropic: Cached inputs are billed at $0.30 / 1M (a 90% discount vs fresh $3.00 / 1M).
- Google Cloud: Gemini 2.5 Pro cached tokens receive a 75% discount ($0.3125 vs $1.25 / 1M).
- OpenAI: GPT-4o cached inputs automatically receive a 50% discount ($1.25 vs $2.50 / 1M).
3. Strategic Multi-Tier Model Routing
High-performing engineering teams rarely route 100% of workload to a single flagship model. A best-practice enterprise pipeline uses a two-stage triage architecture:
- Stage 1 (Classification & Intent Gate): Run Gemini 2.5 Flash or GPT-4o mini to parse input, extract parameters, and classify intent ($0.08 / 1k requests).
- Stage 2 (High-Stakes Reasoning): Route only ambiguous or high-consequence tasks to Claude 3.7 Sonnet or Gemini 2.5 Pro. This hybrid architecture lowers aggregate blend costs by 80% to 92%.