Did you notice much improvement going from Gemini 2.5 to 3? I didn't

I just think they're all struggling to provide real world improvements

Gemini 3 Pro is the first model from Google that I have found usable, and it's very good. It has replaced Claude for me in some cases, but Claude is still my goto for use in coding agents.

(I only access these models via API)

Using it in a specialized subfield of neuroscience, Gemini 3 w/ thinking is a huge leap forward in terms of knowledge and intelligence (with minimal hallucinations). I take it that the majority of people on here are software engineers. If you're evaluating it on writing boilerplate code, you probably have to squint to see differences between the (excellent) raw model performances. whereas in more niche edge cases there is more daylight between them.

what specalized usecases did you use it on and what were the outcomes.

can you share your experience and data for "leap forward" ?

Nearly everyone else (and every measure) seems to have found 3 a big improvement over 2.5.

oh yes im noticing significant improvements across the board but mainly having 1,000,000 token context makes a ton of difference, I can keep digging at a problem with out compaction.

I think what they're actually struggling with is costs. And I think they're all behind the scenes quantizing models to manage load here and there, and they're all giving inconsistent results.

I noticed huge improvement from Sonnet 4.5 to Opus 4.5 when it became unthrottled a couple weeks ago. I wasn't going to sign back up with Anthropic but I did. But two weeks in it's already starting to seem to be inconsistent. And when I go back to Sonnet it feels like they did something to lobotomize it.

Meanwhile I can fire up DeepSeek 3.2 or GLM 4.6 for a fraction of the cost and get almost as good as results.

Maybe they are just more consistent, which is a bit hard to notice immediately.

I noticed a quite noticeable improvement to the point where I made it my go-to model for questions. Coding-wise, not so much. As an intelligent model, writing up designs, investigations, general exploration/research tasks, it's top notch.

yes, 2.5 just couldnt use tools right. 3.0 is way better at coding. better than sonnet 4.5/

Gemini 3 was a massive improvement over 2.5, yes.

I’m curious about if the model has gotten more consistent throughout the full context window? It’s something that OpenAI touted in the release, and I’m curious if it will make a difference for long running tasks or big code reviews.

one positive is that 5.2 is very good at finding bugs but not sure about throughputs I'd imagine it might be improved but haven't seen a real task to benchmark it on.

what I am curious about is 5.2-codex but many of us complained about 5.1-codex (it seemed to get tunnel visioned) and I have been using vanilla 5.1

its just getting very tiring to deal with 5 different permutations of 3 completely separate models but perhaps this is the intent and will keep you on a chase.

The speed bump is nice, but speed alone isn't a compelling upgrade if the qualitative difference isn't obvious in day-to-day use

5.2 is performing worse in technical reading comprehension for information and logic dense puzzles. It's way more confidently wrong and stubborn about understanding definitions of words.