Coding improvements in Gemini 2.5 were driven by strategic shifts in development priorities to deliver real-world value[1]. This involved intensifying focus on incorporating a greater volume and diversity of code data from repository and web sources into the training mixture[1]. There were also substantial enhancements to the evaluation metrics for assessing code capabilities aligned with downstream use cases, alongside improving the ability to predict model performance[1].
Post-training, novel training techniques incorporating reasoning capabilities were developed, and a diverse set of engineering tasks were curated, equipping Gemini with effective problem-solving skills crucial for addressing modern engineering challenges[1]. These advancements are evidenced by superior performance on established benchmarks[1].
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