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A guide explaining how to make agentic workflows up to 462x cheaper by compiling fixed procedures into smaller fine-tuned models instead of repeatedly prompting frontier models.
This paper demonstrates that agentic workflows can be distilled into small fine-tuned models, achieving near-frontier quality while reducing inference cost by two orders of magnitude compared to orchestration approaches.
Empirical study on four 30B-class dense and MoE models showing Gemma-4 26B MoE delivers equal accuracy at 1.9–15 Wh while dense and larger MoE variants consume up to 34 Wh for the same reasoning tasks.