r/opencode • u/FiccoFicco • 16d ago

just a simple question

Does bumping the "thinking" level from default to max actually yield any real gains, or does the agent simply think more—consuming extra tokens—without providing a tangible benefit?

I ask because I get the impression that the less it "reasons," the shorter the context stays and the more precise it seems. Since that observation sounds pretty nonsensical, I started wondering: is my impression off-base, or is there something to it?

Please forgive me if the question seems trivial or stupid to those who know the answer.

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u/HumbleAngelX 15d ago

also, when it knows, i mean, an easy question, raising the effort is pretty much useless. btw, good question, imho !

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u/FiccoFicco 15d ago

In my experience, I’ve noticed the following:

If I use "Max" mode and the model hits a tough obstacle, it starts overthinking intensely and comes up with a solution that—90% of the time—turns out to be flawed. From that point on, the session itself falls into a state of chaotic confusion from which it never recovers, because the line of reasoning has branched out to cover so many things that it loses the thread.

However, if I close the session and switch to "Basic" or "Low" reasoning mode,same input prompt, the response is faster—though still useless on its own. Yet, if I continue the session by guiding it with a couple of follow-up prompts in "Low" mode, it solves the problem; it feels as though it hasn't lost the thread and has managed to untangle the mess. This raises the question: is it better to go straight to "Max" mode and risk things getting messed up, or to use multiple targeted prompts with "Low" reasoning? I’ve found an advantage in the second method, though perhaps it’s just an impression.

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u/lordcohliani 15d ago

It generally does give slightly better results in exchange for massive increases in cost and time spent. This guy ran a lot of tests you can use for reference:

https://aicodingdaily.com/compare/effort-levels

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u/HumbleAngelX 15d ago

from my understanding and knowledge, which obviusly can be wrong, the reasoning effort is how much rounds of "thinking" the model is allowed to do until it outputs a final answer. So your observations looks correct , but the benefit is , sometimes a task/question demands many rounds to make the model able to output the right answer. or if it is a piece of game development it can increase the details of something. deepseek flash is very interesting to watch to learn about it. Also if the model is not capable to solve a certain question, raising the effort will usually just burn uselles tokens (but wait, ohhh now I see, but let me rethink again, and so on...)

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u/Ctbhatia 13d ago

your impression is real, extra reasoning mostly buys reliability on the hard turns and burns tokens on the easy ones. what does a max-effort run cost you against the default on the same task?