Few Word Do Trick

A Patronus AI study of 12 open reasoning models on 20 relatively easy agentic coding tasks found that newer releases of DeepSeek, GLM, and Kimi used substantially less linguistic filler in their reasoning than predecessors, reducing filler-word shares by an average of 41% while matching or modestly improving task success. The analysis classified self-talk, articles, auxiliary verbs, certain connectors, and self-references as filler after excluding code and commands; article use showed the largest reductions, while self-talk declined less consistently. Kimi K3.1 Pro had the leanest reasoning at 9.8% filler, whereas Qwen3.8 Flash was highest at 23.7%, close to the 20.8% measured in human-written SWE-bench bug reports. Terser prose did not reliably reduce cost: DeepSeek V4.1 Flash used 145% more reasoning tokens than its earlier version and Kimi K3.1 Pro used 44% more, while only GLM-5.3-Flash became cheaper. The authors suggest possible explanations including reinforcement-learning length incentives, distillation from terse teacher models, outcome-based training that permits private shorthand, and pressure from structured agent formats, but emphasize that these hypotheses remain unproven and that results may not generalize to harder tasks or other reasoning settings.

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