For the past few days I have been using Gemini 4 Argon and watching it closely. The model itself is good. It gives solid answers and gets the work done. But it has one problem that hurts both the user and the company.
The problem is overthinking. Whether you run it on low, normal or high, and whether the question is small or big, it thinks far more than it needs to. I simply asked it to visit a website and tell me what was on it, or to check an email GitHub had sent to my Gmail. A Flash model does the same job quickly and correctly. But Argon thinks so much that tokens get wasted for no reason. It does not understand the intent behind the question and answer accordingly. It just keeps thinking on its own.
Take a simple example. You ask a straightforward question and the model thinks for a full minute. That causes two losses. First, Google's servers carry load they did not need to. Second, say your usage limit was at 10 percent. That one question pushes it straight to 20 or 25 percent. The tokens are gone, and the answer is the same one you could have got much faster.
Almost every model used to have this problem. OpenAI's GPT models have reduced it a lot, and in my view Claude handles it best, whether it is Opus or Sonnet. I use Claude Opus 5.5 regularly, and for work like software it can compete with Gemini. Even on max effort, if the question is small, it answers right away. It can think deeply too, but it knows when to think and how much, just like we humans do.
One thing I should be clear about. So far I have only used Argon on mobile, not for development, because my current plan does not include it and paying extra for it right now did not seem worth it. If I need it later, I will test it more deeply.
A good model is not the one that thinks the most. A good model is one that gets our work done well at a lower cost. When a model thinks only as much as it needs to, the user saves their usage limit, the company saves server resources and money, and the answer arrives faster. Both sides win.
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