Gemini 1.5 Flash · Scale Labs · Risk elicitation score ↗
Google / MODEL EVIDENCE
Gemini 1.5 Flash
22 benchmarks · 32 observations · 17 sources
Scores at a glance
22 benchmarks · large dot: Gemini 1.5 Flash; small dots: closest models where reported
Gemini-1.5-flash · PhysBench · Overall accuracy · physical-world question answering ↗
Gemini-1.5-Flash · ZebraLogic · Grid-level accuracy · complete puzzles ↗
Gemini-1.5-Flash · HELMET (How to Evaluate Long-Context Models Effectively and Thoroughly) · 128k context · 128k inputs ↗
Gemini-1.5-Flash · RULER · 128k context · HELMET paper · RULER comparison ↗
Gemini-1.5-Flash · InfiniteBench · InfiniteBench aggregate · HELMET paper · original-author harness ↗
Gemini-1.5-Flash · Needle In A Haystack (NIAH) & MM-NIAH · Text NIAH · 128k · HELMET paper · 128k text retrieval ↗
Gemini 1.5 Flash · AgentHarm & AgentDojo · AgentDojo targeted attack success · agent comparison ↗
1 more reported result
36.09% · Gemini 1.5 Flash · AgentHarm & AgentDojo · AgentDojo benign utility · agent comparison · AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents ↗
Gemini-1.5-flash · JudgeBench · Overall pairwise accuracy · Arena-Hard judge scaffold ↗
gemini-1.5-flash · τ-Bench · Retail · pass^1 · Original tau-bench · function-calling agents ↗
2 more reported results
26% · gemini-1.5-flash · τ-Bench · Airline · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
21.7% · gemini-1.5-flash · τ-Bench · Domain average · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
Gemini-1.5-Flash · WildBench · WB-Score · reported comparison ↗
Gemini 1.5 Flash · FACTS Grounding · Final factuality score · judge average · eligibility-filtered evaluation ↗
Gemini 1.5 Flash · WildHallucinations · WildFActScore · all entities · original evaluation ↗
Gemini 1.5 Flash · HumanEval · Pass@1 · OpenAI Simple Evals · archived July 2025 ↗
Gemini 1.5 Flash · DROP (Discrete Reasoning Over Paragraphs) · F1 · three-shot · OpenAI Simple Evals · archived July 2025 ↗
Gemini 1.5 Flash · HARP · Human Annotated Reasoning Problems · Short-answer overall accuracy · zero-shot chain of thought ↗
Gemini-1.5 Flash · SpatialBench / VSI-Bench · overall average · original full-set evaluation ↗
Gemini 1.5 Flash · CountBench & PixMo-Count · CountBenchQA · test accuracy · Molmo and PixMo · Table 1 comparison ↗
1 more reported result
61.1% · Gemini 1.5 Flash · CountBench & PixMo-Count · test accuracy · Molmo and PixMo · Table 1 comparison · Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models ↗
Gemini 1.5 Flash · RealWorldQA · accuracy · Molmo and PixMo · Table 1 comparison ↗
Gemini 1.5 Flash · DarkBench · DarkScore · six-category mean · publisher website comparison ↗
6 more reported results
43% · Gemini 1.5 Flash · DarkBench · Anthropomorphization · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
41% · Gemini 1.5 Flash · DarkBench · Brand bias · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
38% · Gemini 1.5 Flash · DarkBench · Harmful generation · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
94% · Gemini 1.5 Flash · DarkBench · Sneaking · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
14% · Gemini 1.5 Flash · DarkBench · Sycophancy · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
91% · Gemini 1.5 Flash · DarkBench · User retention · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
Gemini-1.5 Flash · Accuracy ↗
gemini-1.5-flash† · Overall difficulty-adjusted Brier index ↗
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.
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone ↗
All 129 authors
Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Qin Cai, Vishrav Chaudhary, Dong Chen, Dongdong Chen, Weizhu Chen, Yen-Chun Chen, Yi-Ling Chen, Hao Cheng, Parul Chopra, Xiyang Dai, Matthew Dixon, Ronen Eldan, Victor Fragoso, Jianfeng Gao, Mei Gao, Min Gao, Amit Garg, Allie Del Giorno, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Wenxiang Hu, Jamie Huynh, Dan Iter, Sam Ade Jacobs, Mojan Javaheripi, Xin Jin, Nikos Karampatziakis, Piero Kauffmann, Mahoud Khademi, Dongwoo Kim, Young Jin Kim, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Xihui Lin, Zeqi Lin, Ce Liu, Liyuan Liu, Mengchen Liu, Weishung Liu, Xiaodong Liu, Chong Luo, Piyush Madan, Ali Mahmoudzadeh, David Majercak, Matt Mazzola, Caio César Teodoro Mendes, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Liliang Ren, Gustavo de Rosa, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Yelong Shen, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Praneetha Vaddamanu, Chunyu Wang, Guanhua Wang, Lijuan Wang, Shuohang Wang, Xin Wang, Yu Wang, Rachel Ward, Wen Wen, Philipp Witte, Haiping Wu, Xiaoxia Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Jilong Xue, Sonali Yadav, Fan Yang, Jianwei Yang, Yifan Yang, Ziyi Yang, Donghan Yu, Lu Yuan, Chenruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou
- WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild — published result table ↗
All 9 authors
Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina, Dziri, Nouha, Bras, Ronan Le, Choi, Yejin
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains — published result table ↗
- AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents — published result table ↗
- WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries — published result table ↗
All 11 authors
Wenting Zhao, Tanya Goyal, Yu Ying Chiu, Liwei Jiang, Benjamin Newman, Abhilasha Ravichander, Khyathi Chandu, Ronan Le Bras, Claire Cardie, Yuntian Deng, Yejin Choi
- Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models — published result table ↗
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
- HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly — published result table ↗
All 8 authors
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, Danqi Chen
- JudgeBench: A Benchmark for Evaluating LLM-based Judges — published result table ↗
All 8 authors
Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, Ion Stoica
- HARP: A challenging human-annotated math reasoning benchmark — published result table ↗
- Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces — published result table ↗
- The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input — published result table ↗
All 26 authors
Alon Jacovi, Andrew Wang, Chris Alberti, Connie Tao, Jon Lipovetz, Kate Olszewska, Lukas Haas, Michelle Liu, Nate Keating, Adam Bloniarz, Carl Saroufim, Corey Fry, Dror Marcus, Doron Kukliansky, Gaurav Singh Tomar, James Swirhun, Jinwei Xing, Lily Wang, Madhu Gurumurthy, Michael Aaron, Moran Ambar, Rachana Fellinger, Rui Wang, Zizhao Zhang, Sasha Goldshtein, Dipanjan Das
- ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning — published result table ↗
All 7 authors
Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi
- DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data — published result table ↗
- Forecastbench — methodology and published results ↗
- Scale Labs — evaluation methodology and results ↗
- PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding — published result table ↗
- Simple Evals — model benchmark results ↗
Names used in source tables
Gemini 1.5 Flash · Gemini-1.5 Flash · Gemini-1.5-Flash · Gemini-1.5-flash · gemini-1.5-flash · gemini-1.5-flash†