"Drawing" the Mona Lisa with GPT-5.6, Claude, Gemini, and Grok

"Drawing" the Mona Lisa with GPT-5.6, Claude, Gemini, and Grok · TryAI All postsWe built a drawing arena: hand a model a blank white canvas and a set of colored-pencil tools, then get out of the way. The model sets a color, tip width, and pressure, lays down batches of strokes, smudges to blend, erases, and calls view_canvas to see its own work and decide what to fix. It either reproduces a target image or draws from a text prompt.

We ran four vision models, GPT-5.6 Sol, Claude Fable 5, Grok 4.5, and Gemini 3.6 Flash, across two targets (the Mona Lisa and Van Gogh's Starry Night, both scored objectively) and five open-ended prompts, for 28 drawings total. We'll cover tool use, cost, output, and whether the models actually improved their work, with our opinion at the end.

Why we run these

A quick note on why we're doing this, because our last one of these (models making music videos) kicked off a good discussion where some folks read it as us promoting the creativity or artistic ability of models. These are not objective tests of model capability. They are deliberately open-ended, fuzzy tasks. Here is why we personally keep running them:

They are fun, and genuinely informative. Watching a model tackle a loose, open-ended task is a more interesting visual indicator of capability than yet another benchmark number.

They expose the real frontier-vs-open gap. Cheaper open-weight models can absolutely replace the frontier for a lot of execution work, and we will keep reporting on that. But there is also a lot of benchmaxxing out there, and tasks like this cut through it. They genuinely separate frontier capability from the rest. Grok 4.5, as you will see, was rough at this "basic" drawing task, and the open-weight models we tried were not even usable, several just returned a blank canvas. (That may change with Kimi K3 which we will report on once it is fully open-sourced)

They show what long-running tasks actually cost. Claude Fable 5 almost always took much longer than the others, for far more money, and here produced worse output. Fable has beaten GPT-5.6 for us on plenty of tasks, including the music-video challenge, but on this one it was worse for a lot more overhead. Depending on your use case, that trade-off matters.

We're loving the discussions. The discussions have genuinely been interesting, and if you have suggestions for the setup or new tasks, we will take them into account. These have been a blast to run, and the outputs are super exciting to see.

The tools

Every model worked with the exact same colored-pencil toolset:

plan : a no-op scratchpad for thinking and planning between steps.

view_target : look at the target image again.

view_canvas : render the current page and see it. This is also when we score the drawing against the target.

set_color / set_brush / set_pressure : the current pencil color, tip width, and pressure (0 to 1 opacity, low pressure for softer tone).

draw : lay down a batch of marks (strokes, lines, shape outlines, dots) in one call. There are no solid fills, tone and color are built by layering, like a real pencil.

smudge : blend/soften a rectangular region, like a blending stump.

erase / clear_canvas : lift a region back to white / reset the whole page.

The whole harness is open source at github.com/hershalb/canvas-arena. Point it at any target image or a text prompt and run it yourself.

The two target reproductions

The models could see the target the whole time and were scored on structural similarity (SSIM) to it. We show the SSIM scores below.

Mona Lisa (target)GPT-5.6 SolClaude Fable 5Grok 4.5Gemini 3.6 Flash Full transcripts: GPT-5.6 Sol · Claude Fable 5 · Grok 4.5 · Gemini 3.6 Flash

Starry Night (target)GPT-5.6 SolClaude Fable 5Grok 4.5Gemini 3.6 Flash Full transcripts: GPT-5.6 Sol · Claude Fable 5 · Grok 4.5 · Gemini 3.6 Flash

The five prompt drawings

No target, no score, just a text brief and a blank page.

"An elderly fisherman's weathered face, warm late-afternoon sun, deep wrinkles and stubble"