RLTasksby ulam.ai
Models / Reported model

Reported model / MODEL EVIDENCE

InternVL2-8B

5 benchmarks · 6 observations · 4 sources

Scores at a glance

5 benchmarks · large dot: InternVL2-8B; small dots: closest models where reported

PhysBench ↗40%
31.6851.94

InternVL2-8B · PhysBench · Overall accuracy · physical-world question answering ↗

RealWorldQA ↗64.2%
45.577.8

InternVL2-8B · RealWorldQA · accuracy · Molmo and PixMo · Table 1 comparison ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

8.3 percentile-point gap

3 of 5 shared benchmark comparisons

How similarity is measured

Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.

Filter comparisons to this model →

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. Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models — published result table ↗

    Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung et al. · 2024

    All 50 authors

    Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung, Salehi, Mohammadreza, Muennighoff, Niklas, Lo, Kyle, Soldaini, Luca, Lu, Jiasen, Anderson, Taira, Bransom, Erin, Ehsani, Kiana, Ngo, Huong, Chen, YenSung, Patel, Ajay, Yatskar, Mark, Callison-Burch, Chris, Head, Andrew, Hendrix, Rose, Bastani, Favyen, VanderBilt, Eli, Lambert, Nathan, Chou, Yvonne, Chheda, Arnavi, Sparks, Jenna, Skjonsberg, Sam, Schmitz, Michael, Sarnat, Aaron, Bischoff, Byron, Walsh, Pete, Newell, Chris, Wolters, Piper, Gupta, Tanmay, Zeng, Kuo-Hao, Borchardt, Jon, Groeneveld, Dirk, Nam, Crystal, Lebrecht, Sophie, Wittlif, Caitlin, Schoenick, Carissa, Michel, Oscar, Krishna, Ranjay, Weihs, Luca, Smith, Noah A., Hajishirzi, Hannaneh, Girshick, Ross, Farhadi, Ali, Kembhavi, Aniruddha

  2. Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces — published result table ↗

    Yang, Jihan, Yang, Shusheng, Gupta, Anjali W., Han, Rilyn, Fei-Fei, Li, Xie, Saining · 2024

  3. MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI — published result table ↗

    Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang et al. · 2024

    All 22 authors

    Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, Jiayi Lei, Quanfeng Lu, Runjian Chen, Peng Xu, Renrui Zhang, Haozhe Zhang, Peng Gao, Yali Wang, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi Shao

  4. PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding — published result table ↗

    Chow, Wei, Mao, Jiageng, Li, Boyi, Seita, Daniel, Guizilini, Vitor, Wang, Yue · 2025

Names used in source tables

InternVL2-8B