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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 and Kimi show significant token efficiency gains over competitors

By PulseAugur Editorial · [1 sources] ·

A comparison of different LLM harnesses shows that GLM 5.3 in Empryo used 51% fewer input tokens than Claude Code, successfully completing 19 out of 19 tasks compared to Claude Code's 17 out of 19. Kimi demonstrated a reduction of 28% to 47% in token usage compared to Kimi CLI, depending on the reasoning level. Codex showed a 17% reduction in tokens but was three times slower. AI

IMPACT Demonstrates potential for reduced inference costs and improved efficiency in LLM applications.

RANK_REASON Comparison of LLM performance and efficiency metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GLM 5.3 and Kimi show significant token efficiency gains over competitors

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Comparison of LLM performance and efficiency metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 Polski(PL) · lukaszslusarski ·

    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…

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

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