BlogLLM Benchmark Scores as Published, Side by Side

ChartQAPro: Results as Published

Every result the catalog holds for ChartQAPro: 5 models from 4 developers, read from 1 page. Each number is as printed by the company named beside it; nothing is ranked or rescored. Models are listed by developer, newest first.

Area Multimodal UnderstandingMaintainer Ahmed Masry and co-authorsIntroduced 2025Data version 2026103001, as of 2026-10-30

ChartQAPro asks a vision-language model to answer questions about chart images. The benchmark has 1,341 charts from 157 sources, including infographics and dashboards, and 1,948 questions, including multiple-choice, conversational, hypothetical, and unanswerable questions. Numeric answers use a relaxed match with a 5 percent margin, except years, which must match exactly. Text answers use ANLS, and multiple-choice and fact-checking answers must match exactly.

ChartQA Pro

ModelValuePrinted bySetup as printed
GPT-6 AstraOpenAI, released 2026-09-0365.1%Mistral AI, Mistral Large 4 (preview)checked 2026-10-15-
DeepSeek-V4.1-FlashDeepSeek, released 2026-09-1059.9%Mistral AI, Mistral Large 4 (preview)checked 2026-10-15-
Kimi K3Moonshot AI, released 2026-07-1661.7%Mistral AI, Mistral Large 4 (preview)checked 2026-10-15-
Mistral Large 4 (preview)Mistral AI, released 2026-10-0663.1%Mistral AI, Mistral Large 4 (preview)checked 2026-10-15-
Mistral Medium 3.5Mistral AI, released 2026-05-2255.4%Mistral AI, Mistral Large 4 (preview)checked 2026-10-15-

Every number here is read from the same catalog file as the benchmark table, data version 2026103001, and is the number the table shows for that model and test. Where one page printed a model under several setups, each value is listed. The catalog is public in armanobosyan/sugra-llm-benchmarks; a wrong number can be reported from the table, with Report an error under the value, or as an issue in that repository.