Comment by gavinray
6 hours ago
Gemini, for whatever reason, has consistently been the best for explaining scientific concepts for me.
Especially with the recent addition of graphic visualization widgets
It manages to strike the perfect balance between depth and style of prose that other Frontier models just can't quite seem to match
I personally find it much better than Claude in day to day tasks. Easier to understand and to the point without getting overly verbose like Claude always does. I solved some pretty difficult engineering problems with it, and most of the time I run Claude in parallel, and more often than not I found Claude to be less precise actually.
This is the only use case I've found it superior to ChatGPT or Claude for - helping me understand higher level math. It's slightly easier to understand than the other models
GPT has gone downhill in terms of prose readability.
On Antrhopic side Claude 5.5 improved over 5, but honestly in this dimension the bar was extremely low.
This is also what keeps me using it. And whether this is rational or not, I feel that if it has a better / more concise understanding of core science/math, it also has a better shot at writing concise and correct code for those topics (a lot of what I do).
https://www.youtube.com/watch?v=NO2I3ueVQrk&lc=UgxRXtipA3QbA...
Comparison with other models.
Do you have some prompt examples I can run. Thanks
I'll publish a few of my convos
1. Sleep debts effect on muscle size and strength gain
https://share.gemini.google/Gvdb5Q02INrl
2. Fish oil and extra cellular vesicles
https://share.gemini.google/fem9EBCe4mfy
3. Pharmacology question about human-equivalent dose
https://share.gemini.google/RWNYJ2eiNjcU
Yea, but you are just accepting AI slop.
Here is your first example, "corrected": https://share.gemini.google/DCSLT4PPap5q
Having an AI do a quick check of research studies is about as effective as just trusting a Vogue magazine article. You are just fooling yourself into thinking there is something useful there, there is not.
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Not OP, but I asked Gemini 3.8 Flash this question (via self-hosted LibreChat) and got an excellent answer:
How can you determine if it's explanation is good if you don't already know the answer? You are not qualified to know if it's answer is correct, by hypothesis.
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Just talk to 3.8 flash about whatever (caveats: ancient knowledge cutoff of jan 2025, small parameter model so if niche '90s korean animated series is your jam, it's gonna be rough). The model is a real joy to talk to, I even will feed it SOTA model output to clean up and clarify for me.
Its the first model ever that I have been worried about a successor losing the "feel" that it has.
Gemini is the best at convincing you that you understand scientific concepts. Unless you have applied that knowledge in other contexts and been given feedback that your understanding is correct you have no idea whether it is good at actually explaining them.
That's generally true for all LLMs, but as with any tool - you can actually use it in a way that produces actual, verifiable results. Obviously it requires a lot of work that 99% of people won't put into what is essentially seen as a 'magic black box', but I do agree with GP that Gemini is quite useful in actually breaking things down in an understandable way, compared to most other models.
My experience with it is mostly using it against an actual, academic source, where I can easily tell if it starts making things up. I wouldn't rely on it for anything that I can't confirm myself.
I had a research question in my area of expertise. Tried it with ChatGPT, Claude, and Gemini. I think all three could have solved the question, but ChatGPT was an unreadable mess with too many sections and headings, Claude got stuck thinking and never produced an output. Gemini gave succinct explanation that I think was similar to ChatGPT conceptually but that I was able to follow and verify. The answer was actually fairly trivial to understand; I just hadn't gone at the problem from that perspective.
I know it is common for people to try AI in domains they aren't experts in, but it is also common for people to use AI in domains they are experts in.