The Benchmark No One Asked For!

2026-07-04 · Source: Matt Wolfe · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The "Busey Bench" is a unique benchmark designed to evaluate AI models' ability to generate visual content through code. Unlike traditional image generation platforms such as DALL-E or Stable Diffusion, this benchmark tasks models with drawing Gary Busey by writing Scalable Vector Graphics (SVG) code, which consists of shapes and lines. The platform allows users to observe the evolution of various AI models' SVG generation capabilities over time, noting improvements or unexpected interpretations since March 2023, when GPT 3.5 Turbo was tested. It also incorporates sorting features to compare models based on cost, token usage, and run duration, alongside a timeline page for tracking provider performance. The entire benchmark and website were developed using Fable.

Key takeaway

For AI Engineers evaluating model capabilities beyond traditional image generation, the Busey Bench highlights the importance of assessing code-based visual output. You should consider how models perform when tasked with generating structured visual code like SVGs, as this reveals different strengths and weaknesses compared to pixel-based synthesis. This benchmark offers a framework to track model evolution and compare efficiency metrics such as cost and token usage for such tasks.

Key insights

A novel benchmark evaluates AI models' capacity for visual code generation, specifically SVG, rather than direct image synthesis.

Principles

• AI model evolution in code-based visual generation can be tracked over time.
• Evaluating AI on code output reveals different capabilities than direct image output.

Method

AI models are prompted to generate SVG code representing a specific image (Gary Busey), with results compared by cost, tokens, and run time.

In practice

• Compare AI model efficiency in generating visual code.
• Monitor how different AI providers improve SVG output.

Topics

• AI Benchmarking
• SVG Generation
• Code Generation
• Model Evaluation
• Fable
• Visual Programming

Best for: Research Scientist, AI Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matt Wolfe.