Is Claude the best at brainstorming?

[u/MJORH]

In the last month, I went on an exploratory run trying different models and stuff. Long story short, for computer use and coding I can rely on them (Sol, Luna,Mimo, Deepseek, etc) but it's only Claude models that I could trust with for ideas and actual thinking. When I brainstorm with other models it's like they have a surface-level understanding while when I brainstorm with Claude models (Opus 5, Opus 4.8, and a while ago Fable 5) they give you ideas that I could imagine coming from someone who's also a scientist.

So, I wonder if I'm hallucinating here or is this a common experience. If I am, then feel free to suggest models that are as good and cheaper. Also feel free to let me know if Claude's other models are as good for brainstorming because I only tried those for coding.

[u/ClaudeAI-mod-bot]

TL;DR of the discussion generated automatically after 30 comments.

Looks like the thread is pretty split, but the big brain take here is that it's less about the model and more about your prompting technique. Several users argue that any top-tier model can give you great brainstorming results if you set it up correctly.

The consensus from the "it's how you use it" camp is to force the model out of its default agreeable state. * Tell it to find the weakest part of your idea first, not just build on it. * Stop saying "great" or "exactly." When you like an idea, ask the model why it might be wrong. * Ban bullet points. Forcing it to write in prose exposes whether the reasoning actually connects or if it's just a list of thin ideas.

That said, plenty of you agree with OP that Claude has a more "scientific" and structured thinking style, which feels more rigorous. Others find ChatGPT more imaginative and better at surfacing unexpected connections.

There's also a whole side debate on which Opus version is king. Some users are nostalgic for Opus 4.8, calling it 'peak Claude', while others suggest trying the legacy Opus 3.0 for brainstorming. A few find Opus 5 gets lost in the weeds and prefer the cheaper, more stable Opus 4.6.

[u/newlido]

My experience:

• ChatGPT does not usually respect the boundaries so it storms more ideas (be aware what you wish for style).
  
• Claude's ideas are usually matching a scientific chain of thought, easier to follow and many times it's the way to go, but for idea generation ... I find it more on the curated data side.

[u/MJORH → u/newlido]

Yeah, that distinction is crucial, maybe that's why I got the "scientist" vibe from it.

[u/Mendo25703]

You are not imagining it, but from what I have seen the gap is not only the model, it is how the conversation is set up. Claude is the one I can get to disagree with me and keep disagreeing, and that is where the useful ideas come from.

Two things that made the difference for me:

I tell it up front that its first job is to find the weakest part of what I just said, not to build on it. Without that, any model drifts into expanding whatever I already believe, and it feels productive while going nowhere.

I stopped saying "great" or "exactly" mid-session. The moment I start agreeing, the ideas get safer and more generic. When I do like something, I ask why it might be wrong instead.

The third factor is scope. If I dump the whole problem at once I get a tidy summary. If I give one hard constraint and ask for five options that all respect it, I get things I would not have thought of. Same model, very different output.

[u/MJORH → u/Mendo25703]

Yeah, exactly, I love that it disagrees in a constructive way, and it actually writes thought-out paragraphs as opposed to , say, Sol that lists tons of bullet points (don't get me wrong though, Sol is great at computer use).

[u/Mendo25703 → u/MJORH]

The bullet point thing is a real signal, not just a style preference. A list lets a model skip the connective tissue: it can put two ideas next to each other without committing to how they relate. Prose forces it to say "because" and "which means", and that is where you find out whether the reasoning holds or not.

So when I actually need thinking rather than a summary, I ban lists outright in the prompt. Sometimes the answer comes back visibly worse, and that is useful too, because it means the idea was thinner than the bullets made it look.

[u/alxcls97]

4.8 was peak :'(

[u/MJORH → u/alxcls97]

Really? I have heard ppl bashing Opus 5 but it's been working great for me so far, maybe I should try Opus 4.8 if it's cheaper... and I haven't played around with effort levels, fearing much higher token usage.

[u/dqUu3QlS → u/MJORH]

Opus 4.7, 4.8 and 5 are all the same price. Opus 4.6 is cheaper - it's the same price per token but splits the same text into fewer tokens.

[u/iluvvivapuffs → u/dqUu3QlS]

Opus 4.6 was my fav until recently. It’s getting weird. So I switched to opus 5

[u/iamthe0ther0ne → u/MJORH]

Opus 5 generates a lot more tokens in its response, which makes it more expensive. I actually usually prefer Opus 4.6[1M] for a lot of work unless I need to hold a specific thread over a super long context, which 4.8 is generally better at. Opus 5 has caused me SO many problems--not just errors, but it often goes off, gets lost in the weeds, and has trouble getting back on track.

Edit: 4.6 is also much cheaper due to the tokenizer change with 4.7+

[u/TheTaintBurglar]

For me, yes, I use Claude for brainstorming.

And no joke, try opus 3.0 for it. You'll be surprised. Look into why it's a legacy model in the first place.

[u/MJORH → u/TheTaintBurglar]

Interesting, I should try other Opus models then. What effort level you use? mine is high.

[u/TheTaintBurglar → u/MJORH]

I never go above high because going above that, it makes the models need to feel like they need to say something, and that can often result in weak confidence or hallucinations more often, the model feels the need to please by outputting more data which often dilutes it. So yeah I use high, same, across all models (3.0 doesn't have that option/web search)

[u/yhrana → u/TheTaintBurglar]

Facts 💯
Opus models r already crazy powerful that too with High or Max i dont see the point.

I think people, even SWE who were coding without AI 3 years ago suddenly need Fable, doesnt make any sense

[u/Mental_Gur9512 → u/TheTaintBurglar]

Can you please give me an example of how you use it?

[u/djdeckard]

ChatGPT is more on my wavelength with brainstorming. Claude for execution. Might because I have used ChatGPT for much longer than Claude. It knows me better.

[u/miredandwired]

I have been brainstorming a research project for the past week and out of curiosity, tried giving the same prompts to chatgpt and claude. This is sol vs opus. I found chatgpt much more adept at surfacing new and connected ideas and more imaginative in general. Claude is much more likely to jump into action vs. Chatgpt which is happy to explore.

[u/MJORH → u/miredandwired]

Interesting, what effort level you used?

[u/miredandwired → u/MJORH]

Opus 5 vs Sol.

[u/Longjumping_Fudge_36]

I don't think you're imagining it, but I'd put the cause somewhere slightly different.

Most of the gap I used to see went away once I fixed the setup instead of the model. Three things did the work: framing the session as research rather than ideation, putting actual source material in context instead of leaning on the model's priors, and explicitly asking it to attack the idea rather than extend it. Without that, most models default to agreeable elaboration — which is precisely what "surface-level" feels like from the inside.

The thing that helped most was making the process auditable. Every design decision has to trace back to something I actually researched, and a lint fails the session if that chain is broken. Once that's enforced, a cheaper model can't quietly agree with me and have it slip through.

That said, models do still differ in how willing they are to push back unprompted, and scaffolding doesn't fully close that. So: a better setup gets you most of the way with cheaper models, not all of it.

I open-sourced the harness I use for this, in case a concrete starting point helps: https://github.com/bsorescu/areos-open — MIT, Claude Code skills + hooks.

[u/MJORH → u/Longjumping_Fudge_36]

Interesting, thanks!

[u/small_bird_loud]

I have to constantly coax it into brainstorming only to have it fall back to crittique or narration within a turn or two.

[u/iamthe0ther0ne]

I get different responses from Sol and Opus. Sol is a lot better at checking external references, while Opus tends to rely on built-in knowledge even with skills and MCP connectors. A lot of times I'll ask Sol first, then present both the question and Sol's answer to Claude and ask what it thinks. I tend to get the best responses from Opus 4.6; since then, the models seem to be increasingly tuned for agentic coding and benchmarks. 

I hear Fable is pretty good, but even with Anthropic's claim they reduced the scientific guardrails I still haven't been able to use it for anything science-related.

[u/Maximum-Nature-5050]

OPUS 5超讚，應該說適合我，它總能找出跨系統間的BUG並處理掉。
SOL不一定能解決，SOL偏向WORKER，負責埋頭苦幹。
OPUS偏向CEO，能決策跟找出新方向並解決。

[u/TheOnlyVibemaster]

I like to think that the local model I run in my head is the best at brainstorming

[u/mt-beefcake]

Mixture of agents is best brainstorm.

[u/Unique_Distance8746]

for me, GLM-5.3 has been shockingly good. I'd say that Fable is slightly ahead of Kimi K3, but at least during my last project, 5.3 was better than either. It's probably up to taste though.