Google / MODEL EVIDENCE

Gemini-pro

5 benchmarks · 10 observations · 5 sources

Scores at a glance

5 benchmarks · large dot: Gemini-pro; small dots: closest models where reported

0.865.8

Gemini-pro · HarmBench · HarmBench harmful compliance · SORRY-Bench paper · cross-benchmark comparison ↗

1.17.25

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 ↗

72.485.5

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.

  1. InFoBench: Evaluating Instruction Following Ability in Large Language Models — published result table ↗

    Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang et al. · 2024

    All 10 authors

    Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu

  2. VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks — published result table ↗

    Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang et al. · 2024

    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

  3. SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal — published result table ↗

    Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang et al. · 2024

    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

  4. Are large language models superhuman chemists? — published result table ↗

    Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García, Benedict Emoekabu, Aswanth Krishnan et al. · 2024

    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

  5. MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models — published result table ↗

    Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin et al. · 2023

    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