AI Cost Calculator

The AI Cost Calculator turns token volumes into a concrete budget estimate for LLM API usage across eight commonly used models from OpenAI, Anthropic, Meta, and Google. Pick a model from the dropdown, enter input tokens per request, output tokens per request, and requests per day, and the tool computes live cost per request plus daily, monthly, and yearly totals. Monthly figures assume thirty days and yearly figures assume three hundred and sixty-five days, exactly as the summary row labels them. Pricing for each model — for example GPT-4o at $5.00 per million input tokens and $15.00 per million output tokens — is baked into the model list, and the hints beside the token fields display the current per-million rates so the arithmetic stays visible. A breakdown table splits spending between input and output components with per-request, per-day, per-month, and per-year columns, ending in a bold total row across all horizons. Inputs are validated as non-negative and requests per day must be greater than zero, with explicit error messages for invalid values and small costs formatted as sub-cent amounts. Because the prices are embedded approximations rather than live quotes, the results are best treated as an order-of-magnitude guide for capacity planning, prompt budgets, and comparing models before committing to a production vendor.

1. 1

   Pick a model in the "Model" dropdown — GPT-4o, GPT-4o mini, GPT-4 Turbo, GPT-3.5 Turbo, Claude 3.5 Sonnet, Claude 3 Haiku, Llama 3.1 70B, or Gemini 1.5 Pro.

2. 2

   Type the average traffic in "Input tokens / req" and "Output tokens / req"; the hints show the selected model's per-million USD rates.

3. 3

   Set "Requests / day" to the expected call volume — zero or negative values draw the "must be greater than zero" error.

4. 4

   Read the Cost / request, Daily cost, Monthly cost, and Yearly cost stat cards for the headline figures.

5. 5

   Scroll the breakdown table to compare per-request, per-day, per-month, and per-year rows for input, output, and the bold total.

• Budgeting a chatbot's monthly API spend before committing to a paid provider tier.
• Comparing GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro at identical volume to pick the most economical fit.
• Deciding whether a cheap model like GPT-4o mini can absorb high-volume, low-complexity traffic.
• Forecasting a year of production inference costs for a runway plan or vendor contract negotiation.
• Measuring how much context trimming or prompt caching saves by re-running with smaller token figures.
• Deriving a defensible per-request cost number to feed into product pricing or per-seat plans.

• Treat all prices as approximations — providers change rates by tier and region, so verify current quotes before finalizing a budget.
• Enter per-request averages, not totals; the tool multiplies by requests per day itself, so double-counting inflates the forecast.
• Remember output tokens dominate cost on premium models, so shortening generation often cuts spend more than trimming input.
• Calibrate token estimates against a real tokenizer; code and multilingual text tokenize very differently from English prose.
• Use the breakdown table to identify which component drives the bill before negotiating a volume discount.
• Read yearly figures as a rough horizon, since usage rarely stays flat across twelve months of a production ramp.

• Halve output tokens on a premium model and re-run to see the strongest cost lever in the breakdown table.
• Set requests per day to your measured peak plus headroom, then read the Yearly cost for runway planning.
• Keep inputs in tokens, not characters — a token is roughly three-quarters of an English word, so calibrate with a tokenizer.
• Skip the premium comparison if latency or quality constraints would force you back to the full model anyway.