Personal telemetry · May 2026 — Oct 2026

A ledger of tokens

Every LLM call I made — split between work and personal use, by model and by harness. Updated weekly.

64% of my LLM usage is work — and 68% of all tokens are served from cache, not recomputed. Most of what I ask has already been asked before.

WorkPersonal
Total tokens
5.9B
980M / month avg
Work share
64%
3.8B work · 2.1B personal
Cache efficiency
68%
served from cache, not recomputed
Peak month
2.5B
Jul 2026
Busiest day
266M
Sep 4, 2026
Yearly trajectory
annual totals
2026baseline
5.9B
Work 3.8BPersonal 2.1B
Monthly volume
tokens / month
0500M1B1.5B2B2.5B
Daily rhythm — last 12 months
work vs personal, own scale each
Work
NovDecJanFebMarAprMayJunJulAugSepOct
lessmore
Personal
NovDecJanFebMarAprMayJunJulAugSepOct
lessmore
Efficiency over time
2026
Cache-read share68.1%baseline
Output : input ratio0.021baseline
Tokens / session4.4M

Lower output:input and rising cache-read share both indicate more reuse and less re-explaining context.

By model
claude-sonnet-51.9B
claude-sonnet-4-6882M
claude-opus-4-7788M
claude-fable-5607M
claude-opus-4-8579M
claude-haiku-4-5437M
claude-opus-5198M
claude-fable-5-1167M
By harness
Copilot CLI3B
Claude Code2.9B
codex_cli7.2M

Bars share one scale; the split shows work vs personal within each row.

Token composition — input · output · cache read · cache write
Work
Input 1.5BOutput 18MCache read 2.1BCache write 133M
Personal
Input 69MOutput 15MCache read 1.9BCache write 161M
Data refreshed weekly from api.bipin.io. Tokens counted across every harness and model I use — work and personal. Last updated October 26, 2026.