# Do more capable models fail more often on long agent tasks?

In one study, yes: the two strongest of ten open-weight models led on long-task completion and also had the highest meltdown rates. Across a 23,392-episode study spanning four task lengths, DeepSeek V3 and MiniMax M2.5 scored highest on the paper's partial-credit measure for very-long tasks (0.87 and 0.89) and melted down on 19% and 13% of very-long episodes, against 0 to 4% for every weaker model tested. A meltdown is a spike in the entropy of the model's tool calls: it starts repeating a tool with minor argument tweaks or chasing a dead-end subtask instead of making coherent progress.

Weaker models melt down less because they never attempt the ambitious multi-step plans that could go wrong; a shallower strategy completes less of the task but also derails less. [Reliability and raw completion rate measure different things](/answers/reliabilitybench/how-do-you-measure-whether-an-agent-succeeds-consistently), and this is a case where the model that finishes the most is also the one most likely to spiral on its worst runs. The study tested open-weight models specifically; whether proprietary frontier models show the same pattern hasn't been tested here.

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Sources:
- Khanal, Tao, and Zhou, "Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents" (arXiv:2603.29231): https://arxiv.org/abs/2603.29231 (fetched 2026-10-24)

Source: https://tessary.ai/answers/agent-reliability/do-more-capable-models-fail-more-often-on-long-agent-tasks
More on Agent reliability: https://tessary.ai/answers/agent-reliability
From Tessary, agent reliability for AI agents in production: https://tessary.ai