Methodology
Same prompt. Same rules. Public receipts.
Every benchmark version preserves its prompt, tool policy, execution route, run date, model release date, model metadata, artifacts, and notes. Manual/native website runs are labeled separately from API runs.
Active Prompts
| Version | Tool policy | Prompt |
|---|---|---|
| svg-v1-no-web | no web | Create a standalone SVG portrait of Gary Busey's face. Output only valid SVG markup. Do not wrap the SVG in Markdown fences. Do not use external images, links, scripts, CSS imports, or remote assets. Make the portrait recognizable as Gary Busey using vector shapes only. Include face shape, hair, eyes, eyebrows, nose, mouth, teeth, and expressive features. Use a 1024 by 1024 viewBox. Use detailed SVG-native vector techniques: layered paths, gradients, masks, clipping paths, shadows, highlights, blur filters, opacity, and fine strokes. The portrait should be as recognizable and detailed as possible. |
| image-v1 | manual unknown | A recognizable editorial portrait of actor Gary Busey, centered face, expressive eyes, broad smile, detailed hair, neutral studio background, high detail, square composition. Do not include text, logos, watermarks, or extra people. |
Your Eyes First, Scores Second
The point of BuseyBench is visual comparison over time: how model releases improve, degrade, or simply get stranger under the same prompt. The AI judge scores described below exist to make that progress sortable and chartable — they are a structured version of the same subjective call your own eyes make, not a claim of objective truth. When a score and your eyes disagree, trust your eyes; the gallery is always the ground truth.
What To Compare
| Model release date | Newest and oldest model sorting uses the model release date, not the date the prompt was run. |
|---|---|
| Prompt version | Runs are only apples-to-apples when they use the same prompt version and tool policy. |
| Execution route | OpenRouter, direct API, native website, and manual uploads are labeled because those surfaces can behave differently. |
| Artifact source | SVG runs expose stored source so the output can be inspected beyond the rendered preview. |
Safety Handling
Model SVG output is treated as untrusted code. The app extracts SVG markup, rejects scripts, event handlers, remote assets, and embedded objects, then stores the sanitized artifact for review before publication.
AI Judge Scoring
Every published output is scored by a panel of vision models from 3 different labs (gpt-5.2, gemini-3.1-pro-preview, claude-sonnet-4.6) — an ensemble, so no single model gets to flatter its own family. Each judge sees the rendered image (SVGs are rasterized at 1024px) and an anchored rubric, and scores four dimensions from 0-10: prompt adherence, face coherence, Busey likeness, and aesthetics. Each judge scores 3× at temperature 0 and we take its median; judges are then averaged.
The overall score is a fixed weighted composite computed by the site, not by the judges: likeness 35%, face coherence 25%, aesthetics 25%, prompt adherence 15%. Leaderboard ordering additionally uses head-to-head matchups: the primary judge compares pairs of outputs twice with positions swapped (disagreement counts as a draw), and a score-anchored Bradley-Terry fit turns the verdicts into an Elo-style rating shown alongside each model. Matchmaking is ladder-style against the current standings: models face the entries ranked closest to them, with same-score tie-breaker matchups first. The leaderboard ranks by overall judge score; when two models display the same score, their head-to-head Elo breaks the tie. Scores are tagged with a rubric version; when the rubric or judge panel changes, everything is re-scored — old and new scores are never mixed.