How the animation coding benchmark works
One shared prompt. Original model code. Independently recorded pixels.
The shared task
Each model creates a twelve-second scene: a Pomeranian runs through a backyard, jumps over a puddle, lands, enters its doghouse, retrieves a bone, backs out and faces the camera. Models choose their own layout, rendering style and lighting. No reference artwork or scene template is supplied.
Seven things to look for
- Pomeranian identity & anatomy
- Grounded running and reverse gait
- Puddle clearance, landing & water reflection
- Doghouse entry & occlusion
- Bone grip & object permanence
- Coherent front-facing turn
- Fur, light & final presentation
Fair settings and recorded evidence
Models accessed through Microsoft Foundry, Google Vertex AI and OpenRouter receive the same initial prompt. OpenRouter additions fix a named model and upstream provider with fallbacks disabled. The shared output allowance is 32,768 tokens, including reasoning, subject to lower provider ceilings. One execution repair is available for empty, truncated or unexecutable code. The final response is kept, not a best-of selection.
Gemini 4 Argon also received a disclosed framing correction with visibility feedback. Both responses are counted. No organizer manually edits model code. Browser checks cover execution, movement, repeatability and offline operation; they do not grade anatomy, visual realism or narrative success.
Tokens and known cost
An offline browser captures 360 frames at 960 × 540, at 30 fps. The site plays those recorded films. Provider-reported output includes reasoning once; known cost uses recorded input and output at dated API rates, or the terminal provider-reported charge for OpenRouter results. Missing receipts or prices remain unavailable, not zero.
An AI coding benchmark you can watch
PomBench compares how language models turn the same creative brief into a working browser animation. Each model writes its own scene and rendering code. Watch the Pomeranian's movement, the puddle jump and the bone retrieval, then compare the results on a shared timeline.
Explore model-specific recordings, output tokens and known API costs. This is an observable animation programming task, with original model output and documented corrections. It is not a text-to-video generator or an overall ranking of AI intelligence.
Read the benchmark methodology ↗