AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Monthly Request Volume
Enter expected monthly API queries or chatbot user interactions.
2. Set Average Prompt & Completion Lengths
Input average input prompt tokens (user query + system context) and expected output completion tokens.
3. Choose Models to Compare
Compare GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3.5 Haiku, Gemini 1.5 Flash, and DeepSeek-V3.
4. Analyze Monthly & Annual Expenditure
Review side-by-side cost projections, cost per request, and hybrid model savings.
Formula or logic used
Multi-Model Inference Pricing Logic
AI API billing separates input prompt tokens from output generation tokens, with output tokens typically priced 3x to 5x higher due to autoregressive generation.
Total Monthly Input Tokens: $T_{\text{in}} = \text{Requests} \times \text{Avg Input Tokens}$Total Monthly Output Tokens: $T_{\text{out}} = \text{Requests} \times \text{Avg Output Tokens}$Model Monthly Bill: $\text{Cost} = (\frac{T_{\text{in}}}{1,000,000} \times P_{\text{in}}) + (\frac{T_{\text{out}}}{1,000,000} \times P_{\text{out}})$Blended Cost per Call = $\frac{\text{Total Monthly Bill}}{\text{Monthly Requests}}$
Examples
Example 1: Customer Support AI Assistant (100k conversations/mo)
Demonstrates that small 'mini' or 'flash' models reduce customer support inference bills by 95%.
Example 2: Complex Code Analysis Agent (10k tasks/mo)
Highlights the cost advantage of DeepSeek and tiered routing for large context coding workloads.
Common use cases
SaaS LLM Budget Forecasting
Accurately forecast variable model costs before launching new generative AI features to paying customers.
Model Routing & Fallback Design
Route simple queries to inexpensive flash models and reserve frontier models for complex reasoning.
Unit Economics Planning
Determine how much to charge for software seats to ensure customer usage does not exceed subscription fees.
OpenAI vs Anthropic vs Google Comparison
Evaluate whether migrating from GPT-4o to Claude 3.5 Sonnet or Gemini 1.5 Flash meets budget targets.
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Frequently asked questions
Why do output completion tokens cost more than input prompt tokens?
Input tokens are processed in parallel via the transformer attention matrix. Output tokens must be generated autoregressively one token at a time, requiring significantly more GPU execution time and memory bandwidth.
What is prompt caching and how does it reduce AI API cost?
Both Anthropic and OpenAI support prompt caching. If your system prompt or reference documents remain unchanged across requests, cached input tokens receive up to an 80-90% price discount and lower latency.
Can I use smaller models like GPT-4o mini or Gemini Flash in production?
Yes! Modern small models achieve accuracy scores comparable to older frontier models (like GPT-4) on standard classification, extraction, and summarization tasks at 5% of the cost.
How do tokens relate to words and characters?
For English text, 1 token is approximately 0.75 words or 4 characters. A 1,000-word article consumes roughly 1,333 tokens.
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