Gemini 3 Pro: the frontier of vision AI

>>xnx+(OP)
Well

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.

>>Workac+cU
I don’t know much about AI, but I have this image test that everything has failed at. You basically just present an image of a maze and ask the LLM to draw a line through the most optimal path.

Here’s how Nano Banana fared: https://x.com/danielvaughn/status/1971640520176029704?s=46

>>daniel+dY
I tried this with Claude:

``` > [Image #1] Create a unicode "ascii-art" version of this image, with the optimal path through the maze highlighted in a solid colour.

I'll create an ASCII art version of this maze with the solution path highlighted!

  ┌─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┐
  ●●│ │ │ │       │   │       │     │ │   │   │ │ │   │
  ├─┤●└─┴─┐ ├───┐ │ ╔═╗ ├─────┤ ╔═══╝ │ ╔═╝ ╔═╝ │ │ ╔═╝ ├─┤
  │ │●●●●●└─┤   │ │ ║ │ │     │ ║     │ ║   ║   │ │ ║   │ │
  │ └───┐●╔═╧═╗ │ └─╢ │ │ ┌─╔═╝ ║ ┌───┤ ╚═╗ ╚═╗ │ └─╢ ┌─┘ │
  │     │●║   ║ │   ║ │ │ │ ║   ║ │   │   ║   ║ │   ║ │   │
  ├───┐ │●╚═╗ ╚═╧═╗ ║ └─┤ │ ║ ╔═╝ └─╔═╧═╗ ╚═╗ ╚═╧═╗ ║ └─┬─┤
  │   │ │●●●└───┐ ║ ║   │ │ ║ ║     ║   ║   ║     ║ ║   │ │
  │ ╔═╝ └───┐●╔═╝ ║ ╚═╗ │ └─╢ ╚═╗ ╔═╝ ╔═╝ ┌─╢ ╔═══╝ ╚═╗ │ │
  │ ║       │●║   ║   ║ │   ║   ║ ║   ║   │ ║ ║       ║ │ │
  │ ╚═══╗ ╔═╝●╚═╗ ╚═╗ ║ ├─╔═╧═╗ ║ ║ ╔═╝ ╔═╝ ║ ║ ╔═╗ ╔═╝ │ │
  │     ║ ║●●●●●│   ║ ║ │ ║   ║ ║ ║ ║   ║   ║ ║ ║ ║ ║   │ │
  ├───╔═╝ ║●┌─┐●└─╗ ║ └─┤ ║ ╔═╝ ║ ╚═╝ ╔═╧═╗ ║ ╚═╝ ║ ╚═╗ │ │
  │   ║   ║●│ │●●●║ ║   │ ║ ║   ║     ║   ║ ║     ║   ║ │ │
  │ ╔═╝ ╔═╝●│ └─┐●╚═╧═╗ │ ║ ║ ╔═╧═══╗ ║ ╔═╝ ╚═══╗ ╚═╗ ║ └─┤
  │ ║   ║●●●│   │●●●●●║ │ ║ ║ ║     ║ ║ ║       ║   ║ ║   │
  │ ║ ╔═╝●┌─┴─╗ └───┐●╚═╝ ║ ║ ║ ╔═╗ ║ └─╢ ╔═══╗ ╚═╗ ╚═╝ ╔═╝
  │ ║ ║●●●│   ║     │●●●●●║ ║ ║ ║ ║ ║   ║ ║   ║   ║     ║
  │ ╚═╝●╔═╧═╗ ╚═╗ ╔═╧═══┐●╚═╝ ║ ║ ║ ╚═╗ ║ ║ ╔═╧═╗ ╚═══╗ ╚═╗
  │   ●●║   ║   ║ ║     │●●●●●║ ║ ║   ║ ║ ║ ║   ║     ║   ║
  ├─╔═┘●╚═╗ ╚═╗ ║ ║ ╔═╗ └───┐●╚═╝ ╚═╗ ║ └─╢ ║ ╔═╧═╗ ╔═╝ ╔═╝
  │ ║●●●●●║   ║ ║ ║ ║ ║     │●●●●●●●║ ║   ║ ║ ║   ║ ║   ║
  │ ║●┌─╗ ╚═╗ ║ └─╢ ║ ╚═══╗ └─────┐●╚═╧═╗ ║ ║ ║ ╔═╝ ║ ╔═╝ ╔
  │ ║●│ ║   ║ ║   ║ ║     ║       │●●●●●║ ║ ║ ║ ║   ║ ║   ║
  │ ╚═╝ ║ ╔═╝ ╚═╗ ║ ╚═╗ ╔═╧═════╗ └───┐●╚═╝ ║ ╚═╝ ╔═╝ ║ ╔═╝
  │     ║ ║     ║ ║   ║ ║       ║     │●●●●●║     ║   ║ ║
  └─────╨─╨─────╨─╨───╨─╨───────╨─────┴───●●╨─────╨───╨─╨──

  The path from the mouse () to the cheese () is marked with ● characters. This shows the optimal route through the maze, navigating through the corridors while avoiding all the walls!

```

Suffice to say, it didn't do either part right.

>>pwagla+j31
Honestly, even though it failed, I'm kind of impressed that the trajectory mostly stays in the lines. If you remove all but two openings, does it work? The drawing you show has more than two openings, some of which are inaccessible from the inside of the maze.

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