Theo Browne reports mixed early experience with Gemini 4 Argon. He finds useful frontend and visual capabilities, including a strong result on his skateboard-trick benchmark, but argues that advertised token prices and headline benchmark scores do not capture the cost or reliability of his actual coding workloads.
Theo Browne compares generated interfaces, a fish-tank game and several codebase audits. His examples show broken game mechanics, inaccurate review findings and an oversized database change that failed checks. These are his selected tests and personal observations, not a universal measure of model quality.
Theo Browne separates task-specific strengths from production usefulness. He criticizes caching, integration instability and the handling of existing solutions, and says he will retain Gemini chiefly for YouTube-related data workflows. He favors verifying generated findings and evaluating models on the particular jobs they must perform.
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