I’ve been a consistent user of “thinking modes” ever since they were introduced in state-of-the-art models. However, I recently had an unexpected discovery: for my use case, the standard Claude 4 Sonnet model often provides a significantly better experience than its “Thinking” counterpart.
I’ve not found any significant downgrade of code quality and noticed three main advantages with the non-thinking version:
Faster Speed: The standard model generates responses much faster. I’ve occasionally seen the thinking model get stuck for several minutes while planning its next step, which disrupts my workflow.
Reduced Hallucination: This is based on my anecdotal experience over the last few days, but the standard model appears to hallucinate less frequently. Specifically, it seems less prone to inventing and attempting to use non-existent APIs or tools.
Cost-Effectiveness: It’s simply cheaper. This allows me to run more agents in parallel without constantly worrying about exhausting my usage quota.
What are your experiences with thinking vs. non-thinking modes? Have you found effective strategies (MCP, Cursor rules, prompts) to make thinking mode much better than non-thinking ones?
non-thinking is honestly the only reason I used Cursor. It is often wrong but you can break down task into fragments and still be successfull thanks to the speed.
Now with thinking, it does 5 times as much of what you ask for and takes forever even on small tasks, unbearable really
Yes, actually using Auto mode, and finding out that it’s been using Sonnet 4 non-thinking for the past 2 weeks really made me see the non-thinking models not as “less powerful” (as I thought they were, because of the benchmarks), but more “straightforward” models instead. It seems we should really only use thinking models when there is a complex, math like problem at hand.
I just created a post on the same subject a few hours ago. I have found much the same as you, that the thinking models are not actually better than the non-thinking, at least for my regular day to day tasks. All three of your key points, are true for me as well.
I’ve been using gpt-5-fast today. Its a thinking model, and its been a lot slower. I’m trying to see if it is a better coding model…yesterday was not a good day with the gpt-5 model. The reduction in my overall speed with the thinking models is definitely frustrating. The gpt-5-fast model is faster than sonnet, and when the LLM is actually doing real work, its speed is nice. But the periodic thinking processes really hamper things, and I’m generally slower today, than when I use claude-4-sonnet (non-thinking.)
One thing I DO like about the GPT-5 models. They are not egregiously over-agreeable, over-positive, overcompensating with kindness, even in their thought processes. I don’t mind kindness, but when you are telling a model it screwed up to a mind-boggling degree and it needs to fix its **** and it says “You are absolutely right! Of course! I need to be less of an idiot! HAHAHA!” it gets a little annoying.
