Gemini Diffusion has several limitations. It shows weaknesses in reasoning tasks, scoring notably lower on benchmarks like the BIG-Bench Extra Hard reasoning test compared to other models, indicating a potential need for architectural tuning in logic-heavy applications[1][5]. Additionally, while it performs well in coding and editing tasks, its speed can be offset by the computational demands of processing long context windows, as diffusion models require recalculating attention for the entire context each pass, leading to higher computational costs[2].
Moreover, it inherits biases from training data and may produce inaccurate or unclear responses. This raises concerns about its reliability and the need for careful use in contexts requiring factual accuracy[3][4].
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