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🤖 AI API Pricing Calculator
Compare the computational pricing models of various foundational Large Language Models.
⚙️ Calculate Now
📊 Usage Metrics (Per Month)
Reqs
In
Out
💸 LLM API Pricing (Per 1 Million Tokens)
$
$
🧠 Advanced Context Caching
%
$
Total Monthly API Cost
0.00
Effective Cost / 1k Reqs
0.00
Caching Savings
0.00
Cost Distribution
Base Input
Output
Cached Input
Standard vs Cached Cost
| Computational Metrics | Calculated Output |
|---|---|
| 📥 Total Input Tokens (Millions) | 0 M |
| 📤 Total Output Tokens (Millions) | 0 M |
| ✨ Cost of Uncached Input Tokens | 0 |
| ⚡ Cost of Cached Input Tokens | 0 |
| 💬 Cost of Generated Output Tokens | 0 |
| 🛑 Final Monthly Infrastructure Cost | 0 |
✨ AI Optimization Verdict
Calculating metrics…
Understanding LLM Context Caching
Standard calculators simply multiply tokens by the base price. However, modern foundational models (like Claude 3.5 Sonnet and GPT-4o) now utilize Prompt Caching. If you are sending the same system instructions, large documents, or RAG context repeatedly, the API provider “caches” those tokens.
Cached tokens are typically billed at a massive discount (often 50% off the base input price). By factoring in your expected cache hit rate, this engine reveals your true optimized infrastructure cost, allowing you to build much larger context windows efficiently.
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