Simon P. Couch published an analysis of energy consumption for AI coding agents, which derives per-session electricity estimates from Claude Code usage logs and public energy-per-token proxies.
What the analysis does and the headline numbers
The investigation combines per-token energy estimates (informed by work from Epoch AI and model pricing data from Anthropic) with real Claude Code session logs to scale from a “median query” to the much heavier workloads typical of coding agents. The most notable figures: a median Claude Code session is estimated at about 41 Wh (roughly 138× a typical single chat prompt), and a median workday running multiple coding-agent instances comes to roughly 1,300 Wh (1.3 kWh).
Those numbers provide a compact sense of scale without revealing all methodological details; the full analysis shows how token types (input, output, cache reads/writes) and model context lengths drive the totals.
Why coding agents cost more energy
Several technical factors multiply token usage in coding-agent workflows:
- Very long system prompts and tool descriptions — Claude Code sessions often start with tens of thousands of tokens of context that are processed repeatedly.
- Frequent tool calls — a single user instruction can trigger multiple tool executions, each returning substantial text that becomes additional input for the model.
- Large context windows — models with 100k–200k token contexts amplify the compute needed per token, and caching behavior adds more reads and writes to account for.
Taken together, these characteristics turn what looks like a handful of messages into hundreds of heavy queries.
Caveats and context
The estimates rely on several assumptions: per-token energy is back-solved from blended Wh values and billing ratios, and public proxies are used where lab-level telemetry is unavailable. Energy impact also crucially depends on the grid mix powering the compute — renewables versus fossil sources change the environmental implications. The analysis includes these caveats and suggests broader strategies (such as supporting clean-energy efforts) for addressing systemic impact.
For readers interested in the detailed token breakdowns, pricing assumptions, and session-level graphs, the full write-up contains the complete data and methodology: https://www.simonpcouch.com/blog/2026-01-20-cc-impact/