Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Gemini 3 Deep Think

blog.google

Post reply on HN
Print view View original

591–600 of 722 posts

Re: Gemini 3 Deep Think

#591

Post by jetter »

lern_too_spel wrote: post #215
jetter wrote: post #133

it is interesting that the video demo is generating .stl model. I run a lot of tests of LLMs generating OpenSCAD code (as I have recently launched https://modelrift.com text-to-CAD AI editor) and Gemini 3 family LLMs are actually giving the best price-to-performance ratio now. But they are very, VERY far from being able to spit out a complex OpenSCAD model in one shot. So, I had to implement a full fledged "screensho…

If you want that to get better, you need to produce a 3d model benchmark and popularize it. You can start with a pelican riding a bicycle with working bicycle.

building a benchmark is a great idea, thanks, maybe I will have a couple of days to spend on this soon

Re: Gemini 3 Deep Think

#592

Post by deviation »

I'm impressed with the Arc-AGI-2 results - though readers beware... They achieved this score at a cost of $13.62 per task.

For context, Opus 4.6's best score is 68.8% - but at a cost of $3.64 per task.

Re: Gemini 3 Deep Think

#593

Post by mileshilles »

thefounder wrote: post #495
lukebechtel wrote: post #4

Arc-AGI-2: 84.6% (vs 68.8% for Opus 4.6) Wow. https://blog.google/innovation-and-ai/models-and-research/ge...

Am I the only one that can’t find Gemini useful except if you want something cheap? I don’t get what was the whole code red about or all that PR. To me I see no reason to use Gemini instead of of GPT and Anthropic combo. I should add that I’ve tried it as chat bot, coding through copilot and also as part of a multi model prompt generation. Gemini was always the worst by a big margin. I see some people saying it is sm…

maybe it depends on the usage, but in my experience most of the times the Gemini produces much better results for coding, especially for optimization parts. The results that were produced by Claude wasn't even near that of Gemini. But again, depends on the task I think.

Re: Gemini 3 Deep Think

#594

Post by matjet »

energy123 wrote: post #525
rob-wagner wrote: post #297

I’ve been using Gemini 3 Pro on a historical document archiving project for an old club. One of the guys had been working on scanning old handwritten minutes books written in German that were challenging to read (1885 through 1974). Anyways, I was getting decent results on a first pass with 50 page chunks but ended up doing 1 page at a time (accuracy probably 95%). For each page, I submit the page for a transcription…

Suggestion: run the identical prompt N times (2 identical calls to Gemini 3.0 Pro + 2 identical calls to GPT 5.2 Thinking), then running some basic text post-processing to see where the 4 responses agree vs disagree. The disagreements (substrings that aren't identical matches) are where scrutiny is needed. But if all 4 agree on some substring it's almost certainly a correct transcription. Wouldn't be too hard to get…

Look what they need to mimic a fraction of [the power of having the logit probabilities exposed so you can actually see where the model is uncertain]

Re: Gemini 3 Deep Think

#595

Post by bytesandbits »

logicprog wrote: post #105

Is it me or is the rate of model release is accelerating to an absurd degree? Today we have Gemini 3 Deep Think and GPT 5.3 Codex Spark. Yesterday we had GLM5 and MiniMax M2.5. Five days before that we had Opus 4.6 and GPT 5.3. Then maybe two weeks I think before that we had Kimi K2.5.

its cause of a chain of events.

Next week Chinese New year -> Chinese labs release all the models at once before it starts -> US labs respond with what they have already prepared

also note that even in US labs a large proportion of researchers and engineers are chinese and many celebrate the Chinese New Year too.

TLDR: Chinese New Year. Happy Horse year everybody!

Re: Gemini 3 Deep Think

#596

Post by ronyfadel »

the_king wrote: post #496

I just tested it on a very difficult Raven matrix, that the old version of DeepThink, as well as GPT 5.2 Pro, Claude Opus 4.6, and pretty much every other model failed at. This version of DeepSeek got it first try. Thinking time was 2 or 3 minutes. The visual reasoning of this class of Gemini models is incredibly impressive.

Deep Think not DeepSeek

Re: Gemini 3 Deep Think

#597

Post by kriro »

xnx wrote: post #15

Google is absolutely running away with it. The greatest trick they ever pulled was letting people think they were behind.

I'd personally bet on Google and Meta in the long run since they have access to the most interesting datasets from their other operations.

Re: Gemini 3 Deep Think

#598

Post by simonw »

toraway wrote: post #488
simonw wrote: post #39

The pelican riding a bicycle is excellent . I think it's the best I've seen. https://simonwillison.net/2026/Feb/12/gemini-3-deep-think/

So, you've said multiple times in the past that you're not concerned about AI labs training for this specific test because if they did, it would be so obviously incongruous that you'd easily spot the manipulation and call them out. Which tbh has never really sat right with me, seemingly placing way too much confidence in your ability to differentiate organic vs. manipulated output in a way I don't think any human cou…

The other SVGs I tried from my private collection of prompts were all similarly impressive.

Re: Gemini 3 Deep Think

#599

Post by andy12_ »

simianwords wrote: post #6

OT but my intuition says that there’s a spectrum - non thinking models - thinking models - best of N models like deep think an gpt pro Each one is of a certain computational complexity. Simplifying a bit, I think they map to - linear, quadratic and n^3 respectively. I think there are certain class of problems that can’t be solved without thinking because it necessarily involves writing in a scratchpad. And same for b…

The difference between thinking and no-thinking models can be a little blurry. For example, when doing coding tasks Anthropic models with no-thinking mode tend to use a lot of comments to act as a scratchpad. In contrast, models in thinking mode don't do this because they don't need to.

Ultimately, the only real difference between no-thinking and thinking models is the amount of tokens used to reach the final answer. Whether those extra scratchpad tokens are between tags or not doesn't really matter.

Re: Gemini 3 Deep Think

#600

Post by fatherwavelet »

dperhar wrote: post #100
throwa356262 wrote: post #92

Earlier quoted context omitted.

I dont agree, Gemini 3 is pretty good, even the Lite version.

What do you use it for and why? Genuinely curious

I use Gemini Pro for basically everything. I just started learning systems biology as I didn't even know this was a subject until it came up in a conversation.

Biology is subject I am quite lacking in but it is unbelievable to me what I have learned in the last few weeks. Not even in what Gemini says exactly but in the text and papers it has led me to.

One major reason is that it has never cut me off until last night. I ran several deep researches yesterday and then finally got cut off in a sprawling 2 hour conversation.

For me it is the first model now that has something new coming out but I haven't extracted all the value from the old model that I am bored with it. I still haven't tried Opus 4.5 let alone 4.6 because I know I will get cut off right when things get rolling.

I don't think I have even logged into ChatGPT in a month now.

Post reply on HN