Vision2Code: leaderboard
Image-to-code generation tests whether a vision-language model (VLM) can recover the structure of an image enough to express it as executable code. Existing benchmarks either focus on narrow visual domains, depend on paired executable reference code, or rely on generic rubrics that miss domain-specific reconstruction errors. We introduce Vision2Code, a reference-code-free benchmark and evaluation
Metric: Avg. (self-reported). Source: benchmarklist.com. Saturation forecast: Around June 2027. 13 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.4 | 4.12 | #79 |
| 2 | GPT-5.4 Mini | 3.85 | #212 |
| 3 | Gemini 3.1 Flash Lite (Preview) | 3.17 | #194 |
| 4 | Kimi K2.5 | 3.06 | #144 |
| 5 | Qwen 3.5 397B A17B | 2.41 | #146 |
| 6 | Qwen 3.5 9B | 1.6 | #379 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=vision2code · How It Works · Data refreshed daily, snapshot 2026-10-20.