ChartFI: leaderboard
Chart descriptions are essential for accessibility, cross-modal retrieval, and assisting readers in extracting insights from complex visualizations. As multimodal large language models (MLLMs) are increasingly adopted for automated chart description generation, a critical question arises: how faithfully and insightfully do these models actually describe charts? Current benchmarks fall short on two
Metric: Acuity (self-reported). Source: benchmarklist.com. Saturation forecast: Around July 2027. 5 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3 Flash | 3.49 | #99 |
| 2 | GPT-5.4 | 3.23 | #79 |
| 3 | Qwen 3.5 Plus | 3.19 | #125 |
| 4 | Qwen 3.5 27B | 2.8 | #187 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=chartfi · How It Works · Data refreshed daily, snapshot 2026-10-20.