LLM Footprint

Estimate the energy and emissions behind your AI use. No token counts? Start with an example.

Joules/token before serving-profile multiplier. Useful for testing new benchmark data.

Comparisons are alternative yardsticks, not amounts to add together. Energy-only benchmarks are separate from emissions.

Example energy estimates are modeled, not measured. The chat and agent assistant presets use illustrative token counts, not measured averages. “This page” uses a rounded estimate of development usage, not recorded token totals. “Major project” uses the reported token totals from Anthropic’s compiler experiment —2B input and 140M output tokens across nearly 2,000 sessions. That does not establish a typical feature size, cache rate, or input/output ratio. Claude Code’s usage documentation separates fresh input, cache reads, and cache writes; include all three in this calculator’s input total. Example cache rates and model classes are assumptions, not measured characteristics of those workloads.

“Agent team project” uses Bun’s reported pre-merge Zig-to-Rust rewrite usage : 5.9B uncached input + 72B cached input = 77.9B total input, and 690M output. Its 92.4% cache share is rounded to 92% to match the whole-percent control. Both sourced projects are exceptional examples, not typical workloads; their reported token totals do not validate this calculator’s energy coefficients.