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AI Prompt and LLM Inference Carbon Calculator
Per-token inference energy and CO2e by model tier.
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About AI Prompt and LLM Inference Carbon Calculator
Free AI footprint tool: tokens or prompts, model tier and data-center grid into kWh and kg CO2e. For developers and AI teams. No signup.
Tier matters more than prompt count
A frontier multimodal query can use twenty times the energy of a distilled small-model call. Routing easy prompts to small models is the fastest carbon cut an AI team can make.
Grid beats everything
The same million tokens emit sixteen times more on a coal grid than on hydro. Region selection swings results more than model choice at the extremes.
📐 Methodology & Reference Accuracy:
Calculations, formulas, and conversions execute locally in your web browser. Output models are benchmarked against official reference standards and updated for 2026.
🔒 This tool runs entirely in your browser. Nothing is uploaded or stored.
Background reading: Ai Prompt Carbon Calculator on Wikipedia. More: all calculators.
Frequently Asked Questions about AI Prompt and LLM Inference Carbon Calculator
How much energy does one AI prompt use?
Roughly 0.5 Wh per thousand tokens on small models, 3 Wh on medium 70B-class, and 12 Wh on frontier models including data-center overhead.
What drives AI emissions most?
Model size first, data-center grid second, token count third. A short frontier prompt can out-emit a long small-model chat.
How do I cut AI footprint?
Route routine prompts to small models, cap max tokens, batch requests, and prefer clean-grid regions.
Are these measured figures?
No. They are illustrative estimates from public research ranges, for planning and journalism rather than reporting.
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