These are measurements from one machine, not a leaderboard. They answer one question: when you grade every session an agent ran, how far did each one actually get?
One laptop, 2,960 sessions, graded
Source: archie stats on the author's laptop, 2026-09-02, over an index of
2,960 sessions. Every figure is copied from that output; the six rows are the
whole index and the percentages sum to 100.
| Rung | What the session left behind | Sessions | Share |
|---|---|---|---|
| 0 | Nothing verified, or still running | 1,490 | 50.3% |
| 1 | The agent said it was done | 7 | 0.2% |
| 2 | Some files on disk changed | 449 | 15.2% |
| 3 | A test or a build passed | 808 | 27.3% |
| 4 | A commit landed in git | 120 | 4.1% |
| 5 | CI or a deploy went green | 86 | 2.9% |
Evidence starts at rung 3. Half of the sessions never got far enough to tell, and rungs 1 and 2 are things an agent can produce without doing much. Only the last three rows left a trace someone else can check: 1,014 sessions, 34.3%, cleared that line.
What the rungs mean, and why rung 3 needs a captured exit code, is in The outcome ladder.
Get the same table for your machine
archie scan
archie stats ladder
archie stats ladder groups the index by model and repository and shows, for
each group with enough sessions, how often it reached each rung. --min-n
sets the floor (default 20), --json gives the rows. The same table is one
MCP call away as stats_ladder, so your agent can read it too.
What this is not
It is not a model benchmark. The groups are one person's models on one
person's repositories, and a rung says what evidence a session left, not
whether the work was good. The opt-in aggregate export that would make
cross-machine comparison possible is designed in
archie-bench.md and not built.