Comment by Someone1234
14 hours ago
One thing I've noticed and HATE, is that when you increase thinking-effort, that seemingly increases response-length. Meaning that X.High is longer than High, which is longer than Medium, etc.
Which is kind of the inverse of how people work; a really smart person can condense difficult ideas into simple[r] terms. Whereas people who struggle speak a lot but say very little.
High/X.High do seem to deliver better quality results, but it sometimes feels like needle-in-haystack extracting that from the word vomit.
> when you increase thinking-effort, that seemingly increases response-length
I use the /sss writing style - synthetic, short and simple - and it helps a lot.
With LLMs, you're still mostly read things "off the tip of the tongue". A better comparison is observing a smart person talking to themselves while working on a tough problem.
EDIT: also there's a reason the dial is called "effort", not "smarts".
I don't think smart people generally solve problems by talking through reasoning steps at a mile a minute. They clear their mind and let the solution come.
Of course I don't know if there's really a way for this to be molded in current LLM's (sounds more like diffusion)
> They clear their mind and let the solution come.
They work on a problem until their brain is full of problem-related concepts. Then something comes. After validation it might be a solution.
Yes, but that should apply to the CoT "thinking", not the final output.
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It's so bad I've made myself a Pi extension that rewrites responses in side by side view using models on Cerebras (insanely fast tps)
It's so bad I made my own chat client for Claude, so I can attach steering prompts in conversation. They are applied just at the end, before the last LLM response, then removed and response kept.
“I have made this letter longer only because I didn’t have the time to make it shorter.” - Blaise Pascal
I just go over the comments with Gemini 3.1 Pro at the end which has a much more normal "voice" and it doesn't lose nuance as a cheap model would. I don't care so much about what Claude writes during the debugging as I just do all the cleanup at the end instead of at every commit.
The higher the effort the more things Claude checks, and it's eager to tell you about all of them
See, this insight it had early on looked like a red hering for a while, but then turned out to be load-bearing. And that's not just a difference in semantics, it changed the whole conclusion (spoiler: it didn't). And Claude is very eager to tell you about this exciting journey
OpenAI has separate dials for verbosity and reasoning_effort (but could still do a better job).
I hate this too, I had to switch to Codex, because the skill to force Claude Code not to think too much about very, very basic things no longer worked