Polski(PL) Ten sam model, te same zadania, inny harness. GLM 5.3 w Empryo zużył o 51% mniej tokenów wejścia niż w Claude Code (19/19 zadań vs 17/19). Kimi: od 28% do 47% m

GLM 5.3和Kimi在token效率上相比竞争对手有显著提升

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-03 07:31

对不同LLM运行环境的比较显示，Empryo中的GLM 5.3比Claude Code少用了51%的输入token，成功完成了19个任务中的19个，而Claude Code完成了17个。Kimi在token使用量上比Kimi CLI减少了28%至47%，具体取决于推理级别。Codex在token使用量上减少了17%，但速度慢了三倍。 AI

影响展示了LLM应用中降低推理成本和提高效率的潜力。

排序理由 LLM性能和效率指标的比较。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; LLM性能和效率指标的比较。[lever_c_demoted from research: ic=1 ai=1.0]
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Mastodon — mastodon.social TIER_1 Polski(PL) · lukaszslusarski · 2026-11-03 07:31

Same model, same tasks, different harness. GLM 5.3 in Empryo used 51% fewer input tokens than in Claude Code (19/19 tasks vs 17/19). Kimi: from 28% to 47% less

Ten sam model, te same zadania, inny harness. GLM 5.3 w Empryo zużył o 51% mniej tokenów wejścia niż w Claude Code (19/19 zadań vs 17/19). Kimi: od 28% do 47% mniej niż kimi CLI, zależnie od poziomu myślenia. Codex: 17% mniej i trzy razy wolniej, więc tam bez zmian. Tokeny liczon…

链接 important.is/ten-sam-model-polowa-tokenow…

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Same model, same tasks, different harness. GLM 5.3 in Empryo used 51% fewer input tokens than in Claude Code (19/19 tasks vs 17/19). Kimi: from 28% to 47% less

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