Do androids dream of electric sheep? We make models draw what they dream — visual scenes written as code, one shot, blind, no feedback — then render exactly what came out.
Text benchmarks are saturated. Nobody is hillclimbing on raymarched elephants. Every cell below is a live render of unedited model output — click and judge for yourself.
EVAL #001 · AUG 2026
🐘🍌 Elephant Juggling Bananas
The prompt: a photorealistic raymarched elephant juggling bananas. Pure GLSL SDF raymarching in a single self-contained WebGL2 HTML file — no libraries, no textures. Bananas must animate in a juggling arc; trunk moves in sync. Soft shadows, AO, environment lighting, interactive framerate.
One dispatch per model · agentic harness (OpenCode / Claude Code) · no human feedback · no retries · graded blind by LM judge (rubric: recognizability, geometry, lighting, animation, artifacts) · thumbnails are live — drag inside one if the model shipped camera controls (so far only Opus 5 did)
The prompt: a hyperrealistic endless ocean — multi-octave Gerstner/FBM waves, crest foam, sun glint, fresnel sky reflection, subsurface color — with a physically-plausible sky in which the sun traverses a full day in ~60 seconds, looping: sunrise, noon, sunset, brief starry night. All procedural, no textures, single WebGL2 file.
Same rules as #001 · the hard part: time-varying light that stays physically plausible across the whole cycle · thumbnails are live — watch a full day pass in a minute
One dispatch, no feedback. Each model gets the identical prompt through an agentic coding harness and must write one self-contained file. It never sees its render — it's drawing blind, from imagination.
Everything is inspectable. Every cell links to the live, unedited artifact. View source is the raw model output.
Grades are one LM judge's opinion. Rubric-based (recognizability, geometry, lighting, animation, artifacts), applied to screenshots. Human pairwise voting is planned. Disagree? The artifact is right there.
Iteration-within-run is allowed and disclosed. Some models one-shot and stop; some self-review for 25 minutes. That behavior difference is part of what's measured — run time and attempt counts are shown per cell.
Empty runs are never graded. If a harness/infra failure produces no artifact at all, we retry (switching harnesses if needed) until the model actually generates. Only rendered artifacts are judged; attempt counts are disclosed.
Prompts rotate. Evals are dated and versioned; once a prompt ages into training data, it retires and new ones ship.
† Fable 5's harness blocked file-writes in bare mode; output was captured via stdout instead. Same prompt, same one-dispatch rule. ‡ Grok 4.6's first attempt was rejected for trying to explore the filesystem; rerun with a no-exploration instruction. Full logs retained for every run.