An AI sales chatbot developer tested two variants of Google's Gemma 4 model against GPT-4o-mini and GPT-4o for generating customer replies in Arabic. The developer found that both Gemma models, a 26B mixture-of-experts and a 31B dense model, initially exhibited reluctance to answer rather than hallucinating. After adding specific prompt rules for Gemma, the mixture-of-experts model improved its grounded answers, while the dense model began producing false-negative refusals, indicating architectural differences might be more influential than model size. AI
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IMPACT Exploratory tests reveal distinct architectural behaviors in Gemma 4 variants, potentially guiding future fine-tuning for specific applications.
RANK_REASON The cluster describes an exploratory test of an open-source model's performance in a specific application, rather than a formal benchmark or official release. [lever_c_demoted from research: ic=1 ai=1.0]