Gemini-pro · HarmBench · HarmBench harmful compliance · SORRY-Bench paper · cross-benchmark comparison ↗
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Gemini-pro
5 benchmarks · 10 observations · 5 sources
Scores at a glance
5 benchmarks · large dot: Gemini-pro; small dots: closest models where reported
Gemini-Pro · VisualWebArena · Overall task success · original agents ↗
3 more reported results
3.85% · Gemini-Pro · VisualWebArena · Overall task success · original agents · VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks ↗
6.04% · Gemini-Pro · VisualWebArena · Overall task success · original agents · VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks ↗
5.71% · Gemini-Pro · VisualWebArena · Overall task success · original agents · VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks ↗
Gemini-Pro · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
45% · Gemini-Pro · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
Gemini Pro · MME & MME-RealWorld · perception score · Official benchmark repository · published comparison ↗
1 more reported result
436.79 points · Gemini Pro · MME & MME-RealWorld · cognition score · Official benchmark repository · published comparison · MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models ↗
gemini-pro · InFoBench · Decomposed requirement following rate · GPT-4-1106 evaluator ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- InFoBench: Evaluating Instruction Following Ability in Large Language Models — published result table ↗
All 10 authors
Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
- VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks — published result table ↗
All 10 authors
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
- SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal — published result table ↗
All 16 authors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal
- Are large language models superhuman chemists? — published result table ↗
All 35 authors
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Mara Schilling-Wilhelmi, Macjonathan Okereke, Anagha Aneesh, Amir Mohammad Elahi, Mehrdad Asgari, Juliane Eberhardt, Hani M. Elbeheiry, María Victoria Gil, Maximilian Greiner, Caroline T. Holick, Christina Glaubitz, Tim Hoffmann, Abdelrahman Ibrahim, Lea C. Klepsch, Yannik Köster, Fabian Alexander Kreth, Jakob Meyer, Santiago Miret, Jan Matthias Peschel, Michael Ringleb, Nicole Roesner, Johanna Schreiber, Ulrich S. Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler, Philippe Schwaller, Kevin Maik Jablonka
- MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models — published result table ↗
All 14 authors
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, Rongrong Ji, Caifeng Shan, Ran He
Names used in source tables
Gemini Pro · Gemini-Pro · Gemini-pro · gemini-pro