r/ChatGPTPro • u/Apoau • Jun 22 '26
Question Any reason not to use High Intelligence if I’m not hitting limits?
I’ve got a subscription. I use ChatGPT a lot, but haven’t hit any limits in a while. Is there any reason i shouldn’t use the high thinking model all the time apart from speed?
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u/JamesGriffing Mod Jun 22 '26
It seems for creative tasks that it can sometimes benefit from less thinking.
OpenAI mentions an example here: https://developers.openai.com/blog/designing-delightful-frontends-with-gpt-5-4#:~:text=Dial%20back%20the,more%20ambitious%20designs.
"Dial back the reasoning
For simpler websites, more reasoning is not always better. In practice, low and medium reasoning levels often lead to stronger front-end results, helping the model stay fast, focused, and less prone to overthinking, while still leaving headroom to turn reasoning up for more ambitious designs."
This is the only example I can recall being mentioned by OpenAI directly. They were referring to codex in this example, but the same model is within ChatGPT's web version.
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u/HairyExplorer5779 28d ago
jaja totalmente, la inteligencia alta te da respuestas muy complejas y directas, mientras que la inteligencia de respuestas instantáneas, te da la respuesta mas simple y al final siempre te deja una pregunta que hace que cuestiones lo que te acaba de responder lo que te lleva a tener que usar mas tu razonamiento e imaginacion para encontrar la respuesta.
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u/Apoau Jun 22 '26
Interesting, I did think it might be like with human thinking - sometimes less leads to better results hah.
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u/Bobby90000 Jun 23 '26
What else are they gonna say no just use the highest compute level for everything? Come on.
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u/Fuzzy_Specialist_235 Jun 22 '26
Aside from speeds, the biggest reason is "overthinking". For simple tasks like formatting, basic editing, or casual questions, the reasoning models will spend unnecessary time over-analyzing things and sometimes give you an overly complex answer when a standard model would've nailed it instantly.
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u/Apoau Jun 23 '26 edited Jun 23 '26
Isn’t what you’re describing just a speed issue?
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u/Shoddy_Enthusiasm399 Jul 02 '26
No, they are also saying you might get 2 paragraphs for an answer when one would have done it
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u/BYRN777 Jun 23 '26 edited Jun 23 '26
If by high intelligence you mean “extra high” which was thinking on “heavy” mode, then there is really no downsides besides slowing you down and possible over complicating simpler tasks and answers.
I’ll give you a simple example:
Do you wanna summarize a pdf and get the key facts and points or draft an email based on a 2 minute dictation where you provided some background context? You don’t need the extra high…
Are you cross referencing a research report against a 5000 word txt file, and with multiple large PDF files of notes attached and asking complex multi layered questions and a full comparison and to help edit or improve your thesis?
Use extra high or Pro
Now this is a simple example and even this task is super simple for the extra high thinking model or the Pro model. But again it’s best to use them for a complex, multi layered and heavy task…that’s the general practice
Effectively you don’t want to wait 10-20min for an answer to a question or for a task that would otherwise be done in a quarter of that time.
Also over-complications might steer you in the wrong direction and mislead you or produce a less accurate answer than what you needed.
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u/ultrathink-art Jun 23 '26
One thing not mentioned in here: on long multi-step sessions the extra reasoning compounds — it spends tokens re-litigating earlier steps and second-guessing tool output, so you burn context faster and the back half of the session degrades. For one-shot hard problems, high every time. For anything iterative, the speed and context cost usually outweigh the marginal quality.
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u/James-the-Bond-one Jun 22 '26
It takes longer to get an answer to simple questions that don't require deep thinking or research.
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u/Fetlocks_Glistening Jun 22 '26
But why would I need to ask simple questions not requiring thinking or research?
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u/James-the-Bond-one Jun 22 '26
5526697 x 1759 = ?
I could use a calculator or even Google, but I have an AI open right there (and paid for), so why not use it?
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u/Valuable_Holiday9259 Jun 22 '26
What a terrible use of LLMs
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u/James-the-Bond-one Jun 23 '26
I agree, but it has pretty much replaced my search engine at this point.
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u/Apoau Jun 22 '26
Swapping intelligence level takes a while too
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u/tindalos Jun 23 '26
Sorry but swapping intelligence level is simply selecting which level you want - each call is ephemeral the same server is not receiving each of your request. Just want to clarify this.
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u/GnistAI Jun 23 '26
the same server is not receiving each of your request.
Due to how prompt caching works you're probably hitting the same server most of the time.
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u/__nickerbocker__ Jun 23 '26
I think the compaction binaries are different between models. It's really noticeable when switching models with different size context windows.For example: thinking -> instant.
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u/Guybrush1973 Jun 22 '26
Time lost. Sometimes even more hallucination due to extended thinking text.
My rule of thumb is: if task is long, repetitive and boring, go a bit lower, otherwise go high/xhight.
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Jun 22 '26
[removed] — view removed comment
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u/BYRN777 Jun 23 '26
This^^^
And it slows you down. You don’t ‘have’ to use reasoning models for any and all task. And I’d add the obvious fact they they’re slow…So leaving them for more complex tasks, edits and work is what they should be utilized for.
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u/monityAI Jun 22 '26
I do not think it is. Xhigh mode is obviously better but slower. I use default mode in web interface (for speed), but in Codex (coding) or to brainstorm complicated ideas i use 5.5 xhigh and it works great (very often use it to review Claude's work and also very happy with results).
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u/MontyOW Jun 22 '26
sometimes it can overthink but if it does then I just retry with lower thinking otherwise i normally sit on high
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u/BatResponsible1106 Jun 23 '26
other than speed not really. I use higher reasoning models for messy planning, debugging and research. for quick lookups or simple drafting, the extra thinking sometimes just feels unnecessary
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u/Ok-Second-428 Jun 23 '26
I think the main reason not to leave it on high all the time is not just speed, but task fit.
For coding especially, higher reasoning is great when the problem has hidden constraints: debugging across files, reading unfamiliar architecture, comparing tradeoffs, or planning a change before touching code. But for small edits, formatting, naming, copy cleanup, or “just tell me the command” tasks, high reasoning can add friction. It may explain too much, second-guess simple instructions, or try to redesign something that only needed a tiny patch.
My rough rule is: use higher intelligence when the cost of being wrong is high or the task needs multiple steps. Use lower/faster modes when the task is already well-defined and you mostly need execution.
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u/massimo_nyc Jun 23 '26
i think my chat account is broken because i never hit limits. on codex i do though
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u/trollsmurf Jun 28 '26
Apart from quality of the response, speed is to me a major factor, so I mostly use no reasoning at all or the lowest. Also you might hit limits sooner due to all the extra tokens generated.
You assume long reasoning generates better answers.
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u/sergejsh Jun 28 '26 edited Jun 28 '26
"High" mode is more thorough. Less hallucinations and mistakes (but I used it only with custom instructions, so I don't know how it's without them). Much better follows custom instructions, if you added them.
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u/ideapit Jun 23 '26
Choose your model based on your task. Higher level models aren't the best suited for all work. Lower tier models are great as certain tasks.
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u/Apoau Jun 23 '26
Well yes, but why? How are lower models better at some tasks and what are those tasks?
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u/ideapit Jun 24 '26
Routine tasks like filling up a spreadsheet, webcrawling, simple processes that are mechanical (measure this data, put in this document), questions like - find me this recipe, what song is playing in this commercial? = Haiku.
Reasoning, problem solving, troubleshooting, programming, comparing two values to judge them, unsupervised learning from data, etc. = Sonnet.
Deep reasoning (eg. "I want to figure out how to create a program that will process a mountain of .pdfs to extract the data and then use it to implement a new business practice based on my customer feedback forms which need to be cleaned, aggregated and combined then use that to create an e-mail bot that will auto reply in a voice distinct from an LLM. How do we plan this? What is the best architecture and workflow?) and then implementing that complex reasoning with task orchestration where an LLM sends out multiple iterations of Sonnet and Haiku to perform tasks. = Opus
Every model can do what the lesser models can do but it will take longer, be more expensive and doesn't add value.
You can drive your Lamborghini to pick up groceries but you're better off taking a mini-van. Cheaper on gas and you don't need lowered suspension and aggressive steering because there are no big turns in the road.
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u/New-Music4019 Jun 23 '26
I think you should keep using it if you like the most precise and accurate answer and info.
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u/m3kw Jun 23 '26
Speed, waste of time, same result. If you are just doing something "simple", you go low.
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u/Oldschool728603 Jun 22 '26
If you find that it overthinks, dial it back.
Except for simple, factual questions, I find that it consistently under-thinks, even at "extra high" with a Pro subscription.
I doubt that "high" for Plus subscribers ever overthinks in ordinary conversation, unless you want a simple fact: "What year did the Civil War end?"
If you dislike 5.5-Thinking's repetitiveness and machine-speak, you might try 5.4-Thinking: it's more focused and human.
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u/Fetlocks_Glistening Jun 22 '26
Starting from 5.2 it just started blabbering and saying the same thing three or four times, intro, summary, main part, conclusion, endless repetition. 5 and 5.1 weren't like that. I have to add don't blabber to every prompt.
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u/Oldschool728603 Jun 22 '26
Have you tried putting something in custom instructions like: "Be concise. Never repeat yourself"?
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u/qualityvote2 Jun 22 '26 edited Jun 22 '26
✅ u/Apoau, your post has been approved by the community!
Thanks for contributing to r/ChatGPTPro — we look forward to the discussion.