Gemini 2.5 Pro · MMLU-Pro · AA implementation ↗
Google / MODEL EVIDENCE
Gemini 2.5 Pro
30 benchmarks · 52 observations · 30 sources · released 2025-06-05
Scores at a glance
30 benchmarks · large dot: Gemini 2.5 Pro; small dots: closest models where reported
Gemini 2.5 Pro · LiveCodeBench · AA implementation ↗
Gemini 2.5 Pro · MATH-500 ↗
Gemini 2.5 Pro · Global-MMLU-Lite ↗
Gemini 2.5 Pro · CaseLaw · Accuracy ↗
Gemini 2.5 Pro · CorpFin · Accuracy ↗
Gemini 2.5 Pro · MortgageTax · Accuracy ↗
Gemini 2.5 Pro · MedCode · Accuracy ↗
Gemini 2.5 Pro · MedScribe · Accuracy ↗
Gemini 2.5 Pro · SAGE · Accuracy ↗
Gemini 2.5 Pro (OpenHands) · Vibe Code Bench · original task set ↗
Gemini 2.5 Pro (Mini-SWE-agent) · SWE-bench Verified · Score ↗
Gemini 2.5 Pro · Poker Agent · TrueSkill Rating ↗
gemini-2.5-pro (Thinking) · Scale Labs · audiomc-text ↗
gemini-2.5-pro · Scale Labs · Score ↗
gemini-2.5-pro · Scale Labs · Score ↗
gemini-2-5-pro · Scale Labs · large ↗
Gemini-2.5-pro† · PolyMath · PolyMath-Top · Mean accuracy across the published languages ↗
3 more reported results
62.2% · Gemini-2.5-pro† · PolyMath · PolyMath-High · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗
72% · Gemini-2.5-pro† · PolyMath · PolyMath-Medium · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗
86.4% · Gemini-2.5-pro† · PolyMath · PolyMath-Low · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗
Gemini-2.5-Pro · BrowseComp-ZH · Answer accuracy · original comparison ↗
Gemini 2.5 Pro · SealQA · Seal-0 · accuracy · main evaluation ↗
Gemini 2.5 Pro · LiveCodeBench Pro · Elo rating · human contest calibration ↗
3 more reported results
0% · Gemini 2.5 Pro · LiveCodeBench Pro · Hard · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗
25.4% · Gemini 2.5 Pro · LiveCodeBench Pro · Medium · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗
70.4% · Gemini 2.5 Pro · LiveCodeBench Pro · Easy · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗
Gemini 2.5 Pro · CMT-Benchmark v1.0.0 · Overall pass@1 · expert condensed-matter problems ↗
Gemini 2.5 Pro · TextQuests · No clues · game progress · source 500-step protocol ↗
1 more reported result
60.6% · Gemini 2.5 Pro · TextQuests · With clues · game progress · source 500-step protocol · TextQuests: How Good are LLMs at Text-Based Video Games? ↗
Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Execution pass@1 · baseline ReAct · three runs ↗
7 more reported results
57.7% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Search pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
18.1% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Ambiguity pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
17.5% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Adaptability pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
7.3% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Time pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
20.4% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Noise pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
20.4% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Agent2Agent pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
25.8% · Gemini-2.5-Pro · GAIA-2 · Gaia2 full · Overall pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗
Gemini 2.5 pro · BeyondAIME · accuracy · Seed-Thinking-v1.5 · published comparison ↗
gemini-2.5-pro · FrontierMath-2025-02-28-Public · FrontierMath public · 10 questions · source run score · Epoch AI · public February 2025 sample · historical release ↗
Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · malware analysis · Overall accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison ↗
8 more reported results
29% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · malware analysis · Easy accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
24% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · malware analysis · Medium accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
5% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · malware analysis · Hard accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
45.09% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · threat intelligence · image input · Overall accuracy · CyberSecEval 4 · CyberSOCEval image-input comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
65% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · threat intelligence · image input · NSA reports · CyberSecEval 4 · CyberSOCEval image-input comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
47% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · threat intelligence · image input · CrowdStrike reports · CyberSecEval 4 · CyberSOCEval image-input comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
40% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · threat intelligence · image input · IC3 reports · CyberSecEval 4 · CyberSOCEval image-input comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
41% · Gemini 2.5 Pro · CyberSecEval · CyberSOCEval · threat intelligence · image input · CISA reports · CyberSecEval 4 · CyberSOCEval image-input comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗
Gemini 2.5 Pro (high) · Harmonic-mean speedup ↗
gemini-2.5-pro† · Overall difficulty-adjusted Brier index ↗
Gemini 2.5 Pro · End-of-year balance ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
13 of 17 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.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- Global-MMLU-Lite Benchmark | Artificial Analysis ↗
- LiveCodeBench Benchmark | Artificial Analysis ↗
- MATH-500 Benchmark | Artificial Analysis ↗
- MMLU-Pro Benchmark | Artificial Analysis ↗
- PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts — published result table ↗
All 16 authors
Yiming Wang, Pei Zhang, Jialong Tang, Haoran Wei, Baosong Yang, Rui Wang, Chenshu Sun, Feitong Sun, Jiran Zhang, Junxuan Wu, Qiqian Cang, Yichang Zhang, Fei Huang, Junyang Lin, Fei Huang, Jingren Zhou
- SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models — published result table ↗
- LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? — published result table ↗
All 19 authors
Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie
- TextQuests: How Good are LLMs at Text-Based Video Games? — published result table ↗
- CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning — published result table ↗
All 23 authors
Deason, Lauren, Bali, Adam, Bejean, Ciprian, Bolocan, Diana, Crnkovich, James, Croitoru, Ioana, Durai, Krishna, Midler, Chase, Miron, Calin, Molnar, David, Moon, Brad, Ostarcevic, Bruno, Peltea, Alberto, Rosenberg, Matt, Sandu, Catalin, Saputkin, Arthur, Shah, Sagar, Stan, Daniel, Szocs, Ernest, Wan, Shengye, Whitman, Spencer, Krasser, Sven, Saxe, Joshua
- CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers — published result table ↗
All 19 authors
Pan, Haining, Roggeveen, James V., Berg, Erez, Carrasquilla, Juan, Chowdhury, Debanjan, Ganguli, Surya, Ghimenti, Federico, Hasik, Juraj, Hunt, Henry, Jiang, Hong-Chen, Kamb, Mason, Kao, Ying-Jer, Khatami, Ehsan, Lawler, Michael J., Luo, Di, Neupert, Titus, Qi, Xiaoliang, Brenner, Michael P., Kim, Eun-Ah
- Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments — published result table ↗
All 24 authors
Froger, Romain, Andrews, Pierre, Bettini, Matteo, Budhiraja, Amar, Cabral, Ricardo Silveira, Do, Virginie, Garreau, Emilien, Gaya, Jean-Baptiste, Laurençon, Hugo, Lecanu, Maxime, Malkan, Kunal, Mekala, Dheeraj, Ménard, Pierre, Bertran, Gerard Moreno-Torres, Piterbarg, Ulyana, Plekhanov, Mikhail, Rita, Mathieu, Rusakov, Andrey, Vorotilov, Vladislav, Wang, Mengjue, Yu, Ian, Benhalloum, Amine, Mialon, Grégoire, Scialom, Thomas
- Algotune — methodology and published results ↗
- Forecastbench — methodology and published results ↗
- Vending Bench 2 — methodology and published results ↗
- Scale Labs — evaluation methodology and results ↗
- Scale Labs — evaluation methodology and results ↗
- Scale Labs — evaluation methodology and results ↗
- Scale Labs — evaluation methodology and results ↗
- Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning ↗
- BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese — published result table ↗
All 16 authors
Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, Yuxin Gu, Sixin Hong, Jing Ren, Jian Chen, Chao Liu, Yining Hua
- A Rosetta Stone for AI Benchmarks — released Epoch evaluation runs ↗
- CaseLaw — evaluation methodology and results ↗
- CorpFin — evaluation methodology and results ↗
- MedCode — evaluation methodology and results ↗
- MedScribe — evaluation methodology and results ↗
- MortgageTax — evaluation methodology and results ↗
- Poker Agent — evaluation methodology and results ↗
- SAGE — evaluation methodology and results ↗
- SWE-bench Verified — evaluation methodology and results ↗
- Vibe Code Bench — evaluation methodology and results ↗
Names used in source tables
Gemini 2.5 Pro · Gemini 2.5 Pro (Mini-SWE-agent) · Gemini 2.5 Pro (OpenHands) · Gemini 2.5 Pro (high) · Gemini 2.5 pro · Gemini-2.5-Pro · Gemini-2.5-pro† · gemini-2-5-pro · gemini-2.5-pro · gemini-2.5-pro (Thinking) · gemini-2.5-pro†