It is the first model to get partial-credit on an LLM image test I have. Which is counting the legs of a dog. Specifically, a dog with 5 legs. This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs.

In fact GPT5 wrote an edge detection script to see where "golden dog feet" met "bright green grass" to prove to me that there were only 4 legs. The script found 5, and GPT-5 then said it was a bug, and adjusted the script sensitivity so it only located 4, lol.

Anyway, Gemini 3, while still being unable to count the legs first try, did identify "male anatomy" (it's own words) also visible in the picture. The 5th leg was approximately where you could expect a well endowed dog to have a "5th leg".

That aside though, I still wouldn't call it particularly impressive.

As a note, Meta's image slicer correctly highlighted all 5 legs without a hitch. Maybe not quite a transformer, but interesting that it could properly interpret "dog leg" and ID them. Also the dog with many legs (I have a few of them) all had there extra legs added by nano-banana.

I gave it a shitty harness and it almost 1 shotted laying out outlets in a room based on a shitty pdf. I think if I gave it better control it could do a huge portion of my coworkers jobs very soon

[0] https://annas-archive.org/blog/critical-window.html

    72.7% Gemini 3 Pro
    11.4% Gemini 2.5 Pro
    49.9% Claude Opus 4.5
    3.50% GPT-5.1

https://arxiv.org/abs/2504.07981

One was two screenshots of a phone screen with chats that are timestamped and it had to take the nth letter of the mth word based on the timestamp. While the type of riddle could be in the training data the ability to OCR this that well and understand the spatial relation to each object perfectly is something I have not seen from other models yet.

Here's the output from two tests I ran:

1. Asking Nano Banana Pro to solve the word search puzzle directly [1].

2. Asking Nano Banana Pro to highlight each word on the grid, with the position of every word included as part of the prompt [2].

The fact that it gets 2 words correct demonstrates meaningful progress, and it seems like we're really close to having a model that can one-shot this problem soon.

There's actually a bit of nuance required to solve this puzzle correctly which an older Gemini model struggled to do without additional nudging. You have to convert the grid or word list to use matching casing (the grid uses uppercase, the word list uses lowercase), and you need to recognize that "soup mix" needs to have the space removed when doing the search.

[0] https://imgur.com/ekwfHrN

[1] https://imgur.com/1nybezU

[2] https://imgur.com/18mK5i5

Prompt: "wine glass full to the brim"

Image generated: 2/3 full wine glass.

True visual and spatial reasoning denied.

Does somebody know how to correctly prompt the model for these tasks or even better provide some docs? The pictures with the pretty markers are appreciated but that section is a bit vague and without references

Hopefully Google pro marries the two together.

I’d be curious to see how well something like this can be distilled down for isolated acceleration on SBCs or consumer kit, because that’s where the billions to be made reside (factories, remote sites, dangerous or sensitive facilities, etc).

I've never hated industry infatuation with a buzzword more.

Making sure you ask correctly how it should give you the info is still lacking in many people's ability

im just a glorified speedreadin' promptin' QA at this point with codex

once it replaces the QA layer its truly over for software dev jobs

future would be a software genie where on aistudio you type: "go make counterstrike 1.6 clone, here is $500, you have two hours"

edit: saw the Screenspot benchmark and holy ** this is an insane jump!!! 11% to 71% even beating Opus 4.5's 50%...chatgpt is at 3.5% and it matches my experience with codex