hermes configure for accurate dollar figures.Successes = delivered messages (Slack delivery_obligations.state=delivered) + cron completions. Cost = sessions.estimated_cost_usd when configured, fallback rate otherwise.
| Source | Sessions | Cost USD |
|---|---|---|
| cli | 171 | 0.0000 |
| cron | 154 | 0.0000 |
| slack | 38 | 2.5477 |
| subagent | 3 | 0.0000 |
configured (estimated_cost_usd)
High (>0.10) = model is delivering dense output. Low (<0.02) = heavy context load, sparse output — likely over-researching or large skill dumps.
| Source | Sessions | Input | Output | Cache read | Ratio |
|---|---|---|---|---|---|
| cli | 171 | 1,904,386 | 226,788 | 3,646,598 | 0.0393 |
| cron | 154 | 8,224,926 | 1,085,879 | 114,549,007 | 0.0088 |
| slack | 38 | 14,131,315 | 1,020,627 | 287,133,235 | 0.0034 |
| subagent | 3 | 132,824 | 29,446 | 958,067 | 0.0263 |
Tool-call distribution: n=366, median=4, p90=52, p99=132, max=569, zero=32.2%
Zero-tool-call sessions are typically single-shot lookups. p90/p99 reveal runaway loops.
Prior data point (2026-09-03): 29% reclaim on clawy 166-msg = 97,000 tok, 22 tool-result messages replaced.
Current window input: 24,393,451 tok. Estimated reclaim: 7,074,100 (29%) or 12,196,725 (50%).
To run the actual A/B: 1 wk compression OFF (compression.proactive_prune_tokens=0) → 1 wk ON (=48000), same workload. Compare total_in deltas.