VisCodeBench: a new benchmark dataset for Text-to-Vis reflecting real-world practice

VisCodeBench is a new text‑to‑visualisation benchmark comprising 2,672 samples across educational, scientific, and general‑audience domains, designed to evaluate how well large language models can generate visualisations from natural‑language requests. The study benchmarks three visualisation systems—Codex, Claude Code, and LIDA—paired with various LLMs on seven metrics covering execution, chart content, and visual quality. Results show that while basic execution is near‑solved, no system reliably reproduces reference data, with the best data similarity at 0.81 and dropping to 0.69 on the hardest subset; system design and model choice both significantly influence performance, and latency remains a practical challenge.

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• llms
• benchmarks