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 liczone lokalnym proxy, kilka przebiegów na zadanie.https://important.is/ten-sam-model-polowa-tokenow-empryo/#AI #LLM #DevTools
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