Show HN: Gemini models are getting better – we can prove it (github.com)

A recent evaluation of Google’s Gemini models has demonstrated significant advancements in performance through a systematic comparison using the Favur autonomous multi-agent software team. Seven distinct Gemini models were tested against a uniform task, which involved creating a simple pygame application. The meticulous setup ensured that the only variable in the results was the model itself. This structure allowed for an unbiased comparison of how different generations handled the same specifications, revealing critical insights into their operational efficiencies. The findings highlight notable improvements in response efficiency and accuracy across the Gemini model generations, particularly when comparing the latest release, Gemini 3.7 Flash, to earlier iterations like 2.5 Flash. For instance, while 2.5 Flash produced a high volume of output reasoning requests, 3.7 Flash completed the task significantly faster with fewer tool failures, indicating enhanced reliability in code execution. Such evaluations not only validate the technological advancements within the AI community but also provide a transparent benchmarking process that can inform future developments in AI/ML models. The data from this evaluation is publicly accessible, empowering researchers to analyze performance metrics thoroughly and fostering an environment of innovation and collaboration in the AI field.