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Ubify Financial Advisory Board
FINANCIAL ANALYSTS & CPA REVIEWERS
About This Tool
This software engineering calculator estimates API billing expenditures for commercial Large Language Model (LLM) providers based on prompt and completion token volumes. Commercial providers price API requests asymmetrically: incoming prompt tokens incur lower rates than generated output completion tokens due to autoregressive decoding compute requirements.\n\n### Supported Model Rates per Million Tokens (USD)\nThe calculator references standard published API rate cards (per $1,000,000\\text{ tokens}$):\n- **OpenAI GPT-4o:** $\$2.50\\text{ input} / \$10.00\\text{ output}$\n- **Anthropic Claude 3.5 Sonnet:** $\$3.00\\text{ input} / \$15.00\\text{ output}$\n- **Google Gemini 1.5 Pro:** $\$1.25\\text{ input} / \$5.00\\text{ output}$\n- **OpenAI GPT-4o-mini:** $\$0.15\\text{ input} / \$0.60\\text{ output}$\n- **Anthropic Claude 3.5 Haiku:** $\$0.80\\text{ input} / \$4.00\\text{ output}$\n\n### Governing Cost Formulas\nGiven daily requests ($N_{\text{req}}$), average prompt tokens ($T_{\text{in}}$), and average output tokens ($T_{\text{out}}$):\n1. **Daily Input Cost:** $\text{Cost}_{\text{in}} = \frac{T_{\text{in}} \times N_{\text{req}} \times \text{Rate}_{\text{in}}}{1,000,000}$\n2. **Daily Output Cost:** $\text{Cost}_{\text{out}} = \frac{T_{\text{out}} \times N_{\text{req}} \times \text{Rate}_{\text{out}}}{1,000,000}$\n3. **Daily Total Cost:** $\text{Cost}_{\text{day}} = \text{Cost}_{\text{in}} + \text{Cost}_{\text{out}}$\n4. **Monthly Projected Spend (30 days):** $\text{Cost}_{\text{month}} = \text{Cost}_{\text{day}} \times 30$\n5. **Cost per 1,000 Requests:** $\text{Cost}_{\text{1k}} = \left( \frac{\text{Cost}_{\text{day}}}{N_{\text{req}}} \right) \times 1,000$\n\n### Worked Numeric Example\nRunning an enterprise customer support bot using **GPT-4o**:\n- Prompt tokens per request ($T_{\text{in}}$): 1,200 tokens\n- Output tokens per request ($T_{\text{out}}$): 600 tokens\n- Daily volume ($N_{\text{req}}$): 5,000 requests per day\n\n**Step-by-Step Calculation:**\n1. Daily Input Cost: $(1,200 \times 5,000 \times 2.50) / 1,000,000 = 15,000,000 / 1,000,000 = \$15.00$.\n2. Daily Output Cost: $(600 \times 5,000 \times 10.00) / 1,000,000 = 30,000,000 / 1,000,000 = \$30.00$.\n3. Total Daily Bill: $\$15.00 + \$30.00 = \$45.00 / \text{day}$.\n4. Monthly Projected Bill: $\$45.00 \times 30 = \$1,350.00 / \text{month}$.\n5. Cost per 1,000 Inferences: $(\$45.00 / 5,000) \times 1,000 = \$9.00$.\n\n### Client-Side Processing Note\nAll token calculations and cost projections calculate in your browser using local JavaScript arithmetic. No API keys, prompt logs, or volume statistics are transmitted to remote servers.
How to Use
- 1
Select the target LLM provider and model tier from the dropdown.
- 2
Enter your average prompt input token count per API invocation.
- 3
Enter your average response output token count generated by the model.
- 4
Specify your anticipated daily request volume.
- 5
Review estimated daily, monthly, and annual expenses alongside cost per 1,000 requests.
Frequently Asked Questions
Why are output tokens more expensive than input tokens across all major providers?
Processing prompt tokens occurs in parallel during the prefill phase, whereas generating output tokens requires sequential autoregressive inference steps that consume significantly more GPU memory bandwidth.
Roughly how many English words equal 1,000 tokens?
In modern byte-pair tokenizers (such as cl100k_base or o200k_base), 1,000 tokens correspond to approximately 750 English words.
Does this calculator store my volume estimates or require an API key?
No. All calculations evaluate entirely on the client side without requiring API authentication or server connections.