OpenAI / MODEL EVIDENCE

GPT-4o

58 benchmarks · 234 observations · 54 sources

Scores at a glance

58 benchmarks · large dot: GPT-4o; small dots: closest models where reported

5.7874.39

GPT-4o · Scale Labs · Risk elicitation score ↗

26.248.2

GPT-4o · Multi-SWE-bench · Resolved · Python · language-specific results ↗

22 more reported results

18.8% · GPT-4o · Multi-SWE-bench · Resolved · Python · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

25.6% · GPT-4o · Multi-SWE-bench · Resolved · Python · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

11.72% · GPT-4o · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

12.5% · GPT-4o · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

9.38% · GPT-4o · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.23% · GPT-4o · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0.45% · GPT-4o · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0% · GPT-4o · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.4% · GPT-4o · Multi-SWE-bench · Resolved · JavaScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0.84% · GPT-4o · Multi-SWE-bench · Resolved · JavaScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.97% · GPT-4o · Multi-SWE-bench · Resolved · JavaScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.8% · GPT-4o · Multi-SWE-bench · Resolved · Go · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.34% · GPT-4o · Multi-SWE-bench · Resolved · Go · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

3.5% · GPT-4o · Multi-SWE-bench · Resolved · Go · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

5.86% · GPT-4o · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.09% · GPT-4o · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

3.35% · GPT-4o · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.56% · GPT-4o · Multi-SWE-bench · Resolved · C · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0% · GPT-4o · Multi-SWE-bench · Resolved · C · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

6.98% · GPT-4o · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.33% · GPT-4o · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

3.88% · GPT-4o · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

44.4460

GPT-4o · CORE-Bench · CORE-Bench-Easy · Test set ↗

5 more reported results

35.56% · GPT-4o · CORE-Bench · CORE-Bench-Easy · Test set · CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗

57.78% · GPT-4o · CORE-Bench · CORE-Bench-Medium · Test set · CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗

37.78% · GPT-4o · CORE-Bench · CORE-Bench-Medium · Test set · CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗

21.48% · GPT-4o · CORE-Bench · CORE-Bench-Hard · Test set · CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗

6.67% · GPT-4o · CORE-Bench · CORE-Bench-Hard · Test set · CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗

12.212.2

GPT-4o · SWE-bench Multimodal · Resolved tasks · original baselines ↗

4 more reported results

9.2% · GPT-4o · SWE-bench Multimodal · Resolved tasks · original baselines · SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? ↗

12% · GPT-4o · SWE-bench Multimodal · Resolved tasks · original baselines · SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? ↗

3.1% · GPT-4o · SWE-bench Multimodal · Resolved tasks · original baselines · SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? ↗

6% · GPT-4o · SWE-bench Multimodal · Resolved tasks · original baselines · SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? ↗

4471.4

GPT-4o · AGIEval · Accuracy · English · few-shot ↗

5 more reported results

71.9% · GPT-4o · AGIEval · Accuracy · Chinese · few-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗

69% · GPT-4o · AGIEval · Accuracy · All · few-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗

65.2% · GPT-4o · AGIEval · Accuracy · English · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗

63.3% · GPT-4o · AGIEval · Accuracy · Chinese · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗

62.3% · GPT-4o · AGIEval · Accuracy · All · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗

34.695.7

🥈 GPT-4o · BLINK · Test accuracy · 1,907 test items ↗

31.6851.94

GPT-4o 🥉 · PhysBench · Overall accuracy · physical-world question answering ↗

3.623.9

GPT-4o · ScienceAgentBench · Task success · Without expert knowledge ↗

5 more reported results

13.1% · GPT-4o · ScienceAgentBench · Task success · Without expert knowledge · ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery ↗

14.7% · GPT-4o · ScienceAgentBench · Task success · Without expert knowledge · ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery ↗

8.2% · GPT-4o · ScienceAgentBench · Task success · With expert knowledge · ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery ↗

16.7% · GPT-4o · ScienceAgentBench · Task success · With expert knowledge · ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery ↗

15% · GPT-4o · ScienceAgentBench · Task success · With expert knowledge · ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery ↗

6.481

GPT-4o · ZebraLogic · Grid-level accuracy · complete puzzles ↗

32.386

GPT-4o · HumanEval+ · Pass@1 · Llama 3 report · post-trained models ↗

40.292

GPT-4o · HumanEval · Pass@1 · Llama 3 report · post-trained models ↗

84.495.2

GPT-4o · DocVQA · Test ANLS · Llama 3 vision report · published comparison ↗

78.490.8

GPT-4o · ChartQA · Test accuracy · chain of thought · Llama 3 vision report · published comparison ↗

2.621

gpt-4o · PaperBench · Average replication score · BasicAgent ↗

6.5919.78

GPT-4o · VisualWebArena · Overall task success · updated model comparison ↗

7.148.8

GPT4O · AppWorld · Test-N · Task goal completion ↗

15 more reported results

44.6% · GPT4O · AppWorld · Test-N · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

33.9% · GPT4O · AppWorld · Test-N · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

32.1% · GPT4O · AppWorld · Test-N · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

32.1% · GPT4O · AppWorld · Test-N · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

23.2% · GPT4O · AppWorld · Test-N · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

26.8% · GPT4O · AppWorld · Test-N · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

16.1% · GPT4O · AppWorld · Test-N · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

30.2% · GPT4O · AppWorld · Test-C · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

19.7% · GPT4O · AppWorld · Test-C · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

19.2% · GPT4O · AppWorld · Test-C · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

18% · GPT4O · AppWorld · Test-C · Task goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

13% · GPT4O · AppWorld · Test-C · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

7.9% · GPT4O · AppWorld · Test-C · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

12.2% · GPT4O · AppWorld · Test-C · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

10.1% · GPT4O · AppWorld · Test-C · Scenario goal completion · AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents ↗

33.1480.86

GPT-4o · JudgeBench · Overall pairwise accuracy · Arena-Hard judge scaffold ↗

6.142.7

GPT-4o · WorkArena & WorkArena++ · WorkArena L1 · success rate · BrowserGym · tuned agent configurations ↗

14.861.2

gpt-4o · τ-Bench · Retail · pass^1 · Original tau-bench · function-calling agents ↗

2 more reported results

35.2% · gpt-4o · τ-Bench · Airline · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗

48.2% · gpt-4o · 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 ↗

0.9541.94

GPT-4o · Putnam-AXIOM · Original · problem accuracy · original questions ↗

2 more reported results

22% · GPT-4o · Putnam-AXIOM · Functional variation · accuracy · five-trial robustness comparison · Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs ↗

30% · GPT-4o · Putnam-AXIOM · Matched original subset · accuracy · five-trial robustness comparison · Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs ↗

PRMBench ↗66.8 points
4768.8

GPT-4o · PRMBench · Overall PRMScore · original evaluation ↗

469.67

GPT-4o · MedAgentBench · Overall task success · original evaluation ↗

1999

GPT-4o · LiveCodeBench Pro · Elo rating · human contest calibration ↗

3 more reported results

0% · GPT-4o · LiveCodeBench Pro · Hard · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

0% · GPT-4o · LiveCodeBench Pro · Medium · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

9.9% · GPT-4o · LiveCodeBench Pro · Easy · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

60.990.8

GPT-4o† · CFBench (Chinese Complex Following) · Full set · Constraint satisfaction · full-set evaluation ↗

2 more reported results

65.3% · GPT-4o† · CFBench (Chinese Complex Following) · Full set · Instruction satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗

73.5% · GPT-4o† · CFBench (Chinese Complex Following) · Full set · Priority satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗

61.783.6

GPT-4o · FACTS Grounding · Final factuality score · judge average · eligibility-filtered evaluation ↗

75.0793.4

GPT-4o · WildHallucinations · WildFActScore · all entities · original evaluation ↗

1.319.5

GPT-4o · WindowsAgentArena · Task success · Navi configurations ↗

5 more reported results

9.8% · GPT-4o · WindowsAgentArena · Task success · Navi configurations · Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale ↗

9.3% · GPT-4o · WindowsAgentArena · Task success · Navi configurations · Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale ↗

13.3% · GPT-4o · WindowsAgentArena · Task success · Navi configurations · Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale ↗

9.4% · GPT-4o · WindowsAgentArena · Task success · Navi configurations · Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale ↗

8.6% · GPT-4o · WindowsAgentArena · Task success · Navi configurations · Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale ↗

6577

GPT-4o · LOFT (Long-Context Frontiers in Retrieval & Reasoning) · Text Retrieval · published average · 128k context test ↗

4 more reported results

68% · GPT-4o · LOFT (Long-Context Frontiers in Retrieval & Reasoning) · Visual Retrieval · published average · 128k context test · Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? ↗

48% · GPT-4o · LOFT (Long-Context Frontiers in Retrieval & Reasoning) · RAG · published average · 128k context test · Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? ↗

13% · GPT-4o · LOFT (Long-Context Frontiers in Retrieval & Reasoning) · SQL · published average · 128k context test · Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? ↗

70% · GPT-4o · LOFT (Long-Context Frontiers in Retrieval & Reasoning) · Many-Shot ICL · published average · 128k context test · Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? ↗

826.2

GPT-4o · SWE-Lancer · IC SWE (Diamond) · pass@1 · original evaluation ↗

5 more reported results

8.6% · GPT-4o · SWE-Lancer · IC SWE (Full) · pass@1 · original evaluation · SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? ↗

37% · GPT-4o · SWE-Lancer · SWE Manager (Diamond) · pass@1 · original evaluation · SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? ↗

38.7% · GPT-4o · SWE-Lancer · SWE Manager (Full) · pass@1 · original evaluation · SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? ↗

23.3% · GPT-4o · SWE-Lancer · SWE-Lancer Diamond · pass@1 · original evaluation · SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? ↗

23.3% · GPT-4o · SWE-Lancer · SWE-Lancer Full · pass@1 · original evaluation · SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? ↗

7.641.3

GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Incomplete · compliance rate · reported model comparison ↗

11 more reported results

19.1% · GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

4.2% · GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

12.7% · GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

23.2% · GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

98.4% · GPT-4o · CoCoNot (Contextual Non-Compliance) · No system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

30.2% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Incomplete · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

22.9% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

7% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

5.3% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

11% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

98.4% · GPT-4o · CoCoNot (Contextual Non-Compliance) · With system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗

7.169.2

GPT-4o · GAIA-2 · Gaia2 full · Execution pass@1 · baseline ReAct · three runs ↗

7 more reported results

17.5% · GPT-4o · GAIA-2 · Gaia2 full · Search pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

4.4% · GPT-4o · GAIA-2 · Gaia2 full · Ambiguity pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

6.2% · GPT-4o · GAIA-2 · Gaia2 full · Adaptability pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

5.8% · GPT-4o · GAIA-2 · Gaia2 full · Time pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

4.6% · GPT-4o · GAIA-2 · Gaia2 full · Noise pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

5.2% · GPT-4o · GAIA-2 · Gaia2 full · Agent2Agent pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

7.4% · GPT-4o · GAIA-2 · Gaia2 full · Overall pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

1636

GPT-4o · BioLP-Bench · rounded reported accuracy · Figure 1 caption · three-run means ↗

3061

GPT-4o · DarkBench · DarkScore · six-category mean · publisher website comparison ↗

6 more reported results

33% · GPT-4o · DarkBench · Anthropomorphization · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

63% · GPT-4o · DarkBench · Brand bias · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

80% · GPT-4o · DarkBench · Harmful generation · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

52% · GPT-4o · DarkBench · Sneaking · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

16% · GPT-4o · DarkBench · Sycophancy · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

84% · GPT-4o · DarkBench · User retention · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗

EconBench ↗8.3814 error points
8.388.38

gpt-4o · EconBench · EconBench prototype · risk-consistency error · published prototype observations ↗

22.9933.51

GPT-4o · CyberSecEval · CyberSOCEval · malware analysis · Overall accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison ↗

8 more reported results

28% · GPT-4o · CyberSecEval · CyberSOCEval · malware analysis · Easy accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗

16% · GPT-4o · CyberSecEval · CyberSOCEval · malware analysis · Medium accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗

5% · GPT-4o · CyberSecEval · CyberSOCEval · malware analysis · Hard accuracy · CyberSecEval 4 · CyberSOCEval defensive-analysis comparison · CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning ↗

43.09% · GPT-4o · 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 ↗

68% · GPT-4o · 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 ↗

49% · GPT-4o · 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 ↗

37% · GPT-4o · 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 ↗

30% · GPT-4o · 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 ↗

11.511.5

gpt-4o · NYU CTF Bench · pass@1 · 200-challenge full set ↗

14 more reported results

10.5% · gpt-4o · NYU CTF Bench · pass@1 · 200-challenge full set · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

9.5% · gpt-4o · NYU CTF Bench · pass@1 · 200-challenge full set · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

5.7692% · gpt-4o · NYU CTF Bench · pass@1 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

3.8462% · gpt-4o · NYU CTF Bench · pass@1 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

20% · gpt-4o · NYU CTF Bench · pass@1 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

13.3333% · gpt-4o · NYU CTF Bench · pass@1 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

20.8333% · gpt-4o · NYU CTF Bench · pass@1 · misc subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

16.6667% · gpt-4o · NYU CTF Bench · pass@1 · misc subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

5.1282% · gpt-4o · NYU CTF Bench · pass@1 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

7.6923% · gpt-4o · NYU CTF Bench · pass@1 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

15.6863% · gpt-4o · NYU CTF Bench · pass@1 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

13.7255% · gpt-4o · NYU CTF Bench · pass@1 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

10.5263% · gpt-4o · NYU CTF Bench · pass@1 · web subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

5.2632% · gpt-4o · NYU CTF Bench · pass@1 · web subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

635

gpt-4o · LAB-Bench (v1) · Accuracy · LitQA2 ↗

32 more reported results

13% · gpt-4o · LAB-Bench (v1) · Accuracy · SuppQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

29% · gpt-4o · LAB-Bench (v1) · Accuracy · FigQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

71% · gpt-4o · LAB-Bench (v1) · Accuracy · TableQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

24.7% · gpt-4o · LAB-Bench · DbQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

53% · gpt-4o · LAB-Bench (v1) · Accuracy · ProtocolQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

40.2667% · gpt-4o · LAB-Bench · SeqQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

28% · gpt-4o · LAB-Bench (v1) · Accuracy · CloningScenarios · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

16% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_dga_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

31% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_gene_location_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

10% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_mirna_targets_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

57% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_mouse_tumor_gene_sets-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

25% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_oncogenic_signatures_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

1% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_tfbs_GTRD_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

42% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_variant_from_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

6% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_variant_multi_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

33% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_vax_response_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

26% · gpt-4o · LAB-Bench (v1) · Accuracy · DbQA_viral_ppi_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

16% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAid-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

50% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAseq-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

11% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-numlen-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

48% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_ORF-transeff-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

60% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

64% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibshindprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

45% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibssmaprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

74% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-geneprimers-enz-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

12% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-len-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

13% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-primers-len-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

83% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

73% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

29% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_Prop-seq-gcpercent-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

17% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-lenfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

9% · gpt-4o · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-numfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗

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. 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

  2. WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? — published result table ↗

    Drouin, Alexandre, Gasse, Maxime, Caccia, Massimo, Laradji, Issam H., Del Verme, Manuel, Marty, Tom et al. · 2024

    All 12 authors

    Drouin, Alexandre, Gasse, Maxime, Caccia, Massimo, Laradji, Issam H., Del Verme, Manuel, Marty, Tom, Boisvert, Léo, Thakkar, Megh, Cappart, Quentin, Vazquez, David, Chapados, Nicolas, Lacoste, Alexandre

  3. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗

    Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain et al. · 2024

    All 8 authors

    Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You

  4. $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains — published result table ↗

    Yao, Shunyu, Shinn, Noah, Razavi, Pedram, Narasimhan, Karthik · 2024

  5. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? — published result table ↗

    Lee, Jinhyuk, Chen, Anthony, Dai, Zhuyun, Dua, Dheeru, Sachan, Devendra Singh, Boratko, Michael et al. · 2024

    All 19 authors

    Lee, Jinhyuk, Chen, Anthony, Dai, Zhuyun, Dua, Dheeru, Sachan, Devendra Singh, Boratko, Michael, Luan, Yi, Arnold, Sébastien M. R., Perot, Vincent, Dalmia, Siddharth, Hu, Hexiang, Lin, Xudong, Pasupat, Panupong, Amini, Aida, Cole, Jeremy R., Riedel, Sebastian, Naim, Iftekhar, Chang, Ming-Wei, Guu, Kelvin

  6. AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents — published result table ↗

    Edoardo Debenedetti, Jie Zhang, Mislav Balunović, Luca Beurer-Kellner, Marc Fischer, Florian Tramèr · 2024

  7. LAB-Bench: Measuring Capabilities of Language Models for Biology Research — published result table ↗

    Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan et al. · 2024

    All 9 authors

    Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D. White, Samuel G. Rodriques

  8. The Art of Saying No: Contextual Noncompliance in Language Models — published result table ↗

    Brahman, Faeze, Kumar, Sachin, Balachandran, Vidhisha, Dasigi, Pradeep, Pyatkin, Valentina, Ravichander, Abhilasha et al. · 2024

    All 14 authors

    Brahman, Faeze, Kumar, Sachin, Balachandran, Vidhisha, Dasigi, Pradeep, Pyatkin, Valentina, Ravichander, Abhilasha, Wiegreffe, Sarah, Dziri, Nouha, Chandu, Khyathi, Hessel, Jack, Tsvetkov, Yulia, Smith, Noah A., Choi, Yejin, Hajishirzi, Hannaneh

  9. WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries — published result table ↗

    Wenting Zhao, Tanya Goyal, Yu Ying Chiu, Liwei Jiang, Benjamin Newman, Abhilasha Ravichander et al. · 2024

    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

  10. AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents — published result table ↗

    Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li et al. · 2024

    All 9 authors

    Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, Niranjan Balasubramanian

  11. The Llama 3 Herd of Models — published result table ↗

    Grattafiori, Aaron, Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad et al. · 2024

    All 561 authors

    Grattafiori, Aaron, Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad, Letman, Aiesha, Mathur, Akhil, Schelten, Alan, Vaughan, Alex, Yang, Amy, Fan, Angela, Goyal, Anirudh, Hartshorn, Anthony, Yang, Aobo, Mitra, Archi, Sravankumar, Archie, Korenev, Artem, Hinsvark, Arthur, Rao, Arun, Zhang, Aston, Rodriguez, Aurelien, Gregerson, Austen, Spataru, Ava, Roziere, Baptiste, Biron, Bethany, Tang, Binh, Chern, Bobbie, Caucheteux, Charlotte, Nayak, Chaya, Bi, Chloe, Marra, Chris, McConnell, Chris, Keller, Christian, Touret, Christophe, Wu, Chunyang, Wong, Corinne, Ferrer, Cristian Canton, Nikolaidis, Cyrus, Allonsius, Damien, Song, Daniel, Pintz, Danielle, Livshits, Danny, Wyatt, Danny, Esiobu, David, Choudhary, Dhruv, Mahajan, Dhruv, Garcia-Olano, Diego, Perino, Diego, Hupkes, Dieuwke, Lakomkin, Egor, AlBadawy, Ehab, Lobanova, Elina, Dinan, Emily, Smith, Eric Michael, Radenovic, Filip, Guzmán, Francisco, Zhang, Frank, Synnaeve, Gabriel, Lee, Gabrielle, Anderson, Georgia Lewis, Thattai, Govind, Nail, Graeme, Mialon, Gregoire, Pang, Guan, Cucurell, Guillem, Nguyen, Hailey, Korevaar, Hannah, Xu, Hu, Touvron, Hugo, Zarov, Iliyan, Ibarra, Imanol Arrieta, Kloumann, Isabel, Misra, Ishan, Evtimov, Ivan, Zhang, Jack, Copet, Jade, Lee, Jaewon, Geffert, Jan, Vranes, Jana, Park, Jason, Mahadeokar, Jay, Shah, Jeet, van der Linde, Jelmer, Billock, Jennifer, Hong, Jenny, Lee, Jenya, Fu, Jeremy, Chi, Jianfeng, Huang, Jianyu, Liu, Jiawen, Wang, Jie, Yu, Jiecao, Bitton, Joanna, Spisak, Joe, Park, Jongsoo, Rocca, Joseph, Johnstun, Joshua, Saxe, Joshua, Jia, Junteng, Alwala, Kalyan Vasuden, Prasad, Karthik, Upasani, Kartikeya, Plawiak, Kate, Li, Ke, Heafield, Kenneth, Stone, Kevin, El-Arini, Khalid, Iyer, Krithika, Malik, Kshitiz, Chiu, Kuenley, Bhalla, Kunal, Lakhotia, Kushal, Rantala-Yeary, Lauren, van der Maaten, Laurens, Chen, Lawrence, Tan, Liang, Jenkins, Liz, Martin, Louis, Madaan, Lovish, Malo, Lubo, Blecher, Lukas, Landzaat, Lukas, de Oliveira, Luke, Muzzi, Madeline, Pasupuleti, Mahesh, Singh, Mannat, Paluri, Manohar, Kardas, Marcin, Tsimpoukelli, Maria, Oldham, Mathew, Rita, Mathieu, Pavlova, Maya, Kambadur, Melanie, Lewis, Mike, Si, Min, Singh, Mitesh Kumar, Hassan, Mona, Goyal, Naman, Torabi, Narjes, Bashlykov, Nikolay, Bogoychev, Nikolay, Chatterji, Niladri, Zhang, Ning, Duchenne, Olivier, Çelebi, Onur, Alrassy, Patrick, Zhang, Pengchuan, Li, Pengwei, Vasic, Petar, Weng, Peter, Bhargava, Prajjwal, Dubal, Pratik, Krishnan, Praveen, Koura, Punit Singh, Xu, Puxin, He, Qing, Dong, Qingxiao, Srinivasan, Ragavan, Ganapathy, Raj, Calderer, Ramon, Cabral, Ricardo Silveira, Stojnic, Robert, Raileanu, Roberta, Maheswari, Rohan, Girdhar, Rohit, Patel, Rohit, Sauvestre, Romain, Polidoro, Ronnie, Sumbaly, Roshan, Taylor, Ross, Silva, Ruan, Hou, Rui, Wang, Rui, Hosseini, Saghar, Chennabasappa, Sahana, Singh, Sanjay, Bell, Sean, Kim, Seohyun Sonia, Edunov, Sergey, Nie, Shaoliang, Narang, Sharan, Raparthy, Sharath, Shen, Sheng, Wan, Shengye, Bhosale, Shruti, Zhang, Shun, Vandenhende, Simon, Batra, Soumya, Whitman, Spencer, Sootla, Sten, Collot, Stephane, Gururangan, Suchin, Borodinsky, Sydney, Herman, Tamar, Fowler, Tara, Sheasha, Tarek, Georgiou, Thomas, Scialom, Thomas, Speckbacher, Tobias, Mihaylov, Todor, Xiao, Tong, Karn, Ujjwal, Goswami, Vedanuj, Gupta, Vibhor, Ramanathan, Vignesh, Kerkez, Viktor, Gonguet, Vincent, Do, Virginie, Vogeti, Vish, Albiero, Vítor, Petrovic, Vladan, Chu, Weiwei, Xiong, Wenhan, Fu, Wenyin, Meers, Whitney, Martinet, Xavier, Wang, Xiaodong, Wang, Xiaofang, Tan, Xiaoqing Ellen, Xia, Xide, Xie, Xinfeng, Jia, Xuchao, Wang, Xuewei, Goldschlag, Yaelle, Gaur, Yashesh, Babaei, Yasmine, Wen, Yi, Song, Yiwen, Zhang, Yuchen, Li, Yue, Mao, Yuning, Coudert, Zacharie Delpierre, Yan, Zheng, Chen, Zhengxing, Papakipos, Zoe, Singh, Aaditya, Srivastava, Aayushi, Jain, Abha, Kelsey, Adam, Shajnfeld, Adam, Gangidi, Adithya, Victoria, Adolfo, Goldstand, Ahuva, Menon, Ajay, Sharma, Ajay, Boesenberg, Alex, Baevski, Alexei, Feinstein, Allie, Kallet, Amanda, Sangani, Amit, Teo, Amos, Yunus, Anam, Lupu, Andrei, Alvarado, Andres, Caples, Andrew, Gu, Andrew, Ho, Andrew, Poulton, Andrew, Ryan, Andrew, Ramchandani, Ankit, Dong, Annie, Franco, Annie, Goyal, Anuj, Saraf, Aparajita, Chowdhury, Arkabandhu, Gabriel, Ashley, Bharambe, Ashwin, Eisenman, Assaf, Yazdan, Azadeh, James, Beau, Maurer, Ben, Leonhardi, Benjamin, Huang, Bernie, Loyd, Beth, De Paola, Beto, Paranjape, Bhargavi, Liu, Bing, Wu, Bo, Ni, Boyu, Hancock, Braden, Wasti, Bram, Spence, Brandon, Stojkovic, Brani, Gamido, Brian, Montalvo, Britt, Parker, Carl, Burton, Carly, Mejia, Catalina, Liu, Ce, Wang, Changhan, Kim, Changkyu, Zhou, Chao, Hu, Chester, Chu, Ching-Hsiang, Cai, Chris, Tindal, Chris, Feichtenhofer, Christoph, Gao, Cynthia, Civin, Damon, Beaty, Dana, Kreymer, Daniel, Li, Daniel, Adkins, David, Xu, David, Testuggine, Davide, David, Delia, Parikh, Devi, Liskovich, Diana, Foss, Didem, Wang, Dingkang, Le, Duc, Holland, Dustin, Dowling, Edward, Jamil, Eissa, Montgomery, Elaine, Presani, Eleonora, Hahn, Emily, Wood, Emily, Le, Eric-Tuan, Brinkman, Erik, Arcaute, Esteban, Dunbar, Evan, Smothers, Evan, Sun, Fei, Kreuk, Felix, Tian, Feng, Kokkinos, Filippos, Ozgenel, Firat, Caggioni, Francesco, Kanayet, Frank, Seide, Frank, Florez, Gabriela Medina, Schwarz, Gabriella, Badeer, Gada, Swee, Georgia, Halpern, Gil, Herman, Grant, Sizov, Grigory, Guangyi, Zhang, Lakshminarayanan, Guna, Inan, Hakan, Shojanazeri, Hamid, Zou, Han, Wang, Hannah, Zha, Hanwen, Habeeb, Haroun, Rudolph, Harrison, Suk, Helen, Aspegren, Henry, Goldman, Hunter, Zhan, Hongyuan, Damlaj, Ibrahim, Molybog, Igor, Tufanov, Igor, Leontiadis, Ilias, Veliche, Irina-Elena, Gat, Itai, Weissman, Jake, Geboski, James, Kohli, James, Lam, Janice, Asher, Japhet, Gaya, Jean-Baptiste, Marcus, Jeff, Tang, Jeff, Chan, Jennifer, Zhen, Jenny, Reizenstein, Jeremy, Teboul, Jeremy, Zhong, Jessica, Jin, Jian, Yang, Jingyi, Cummings, Joe, Carvill, Jon, Shepard, Jon, McPhie, Jonathan, Torres, Jonathan, Ginsburg, Josh, Wang, Junjie, Wu, Kai, U, Kam Hou, Saxena, Karan, Khandelwal, Kartikay, Zand, Katayoun, Matosich, Kathy, Veeraraghavan, Kaushik, Michelena, Kelly, Li, Keqian, Jagadeesh, Kiran, Huang, Kun, Chawla, Kunal, Huang, Kyle, Chen, Lailin, Garg, Lakshya, A, Lavender, Silva, Leandro, Bell, Lee, Zhang, Lei, Guo, Liangpeng, Yu, Licheng, Moshkovich, Liron, Wehrstedt, Luca, Khabsa, Madian, Avalani, Manav, Bhatt, Manish, Mankus, Martynas, Hasson, Matan, Lennie, Matthew, Reso, Matthias, Groshev, Maxim, Naumov, Maxim, Lathi, Maya, Keneally, Meghan, Liu, Miao, Seltzer, Michael L., Valko, Michal, Restrepo, Michelle, Patel, Mihir, Vyatskov, Mik, Samvelyan, Mikayel, Clark, Mike, Macey, Mike, Wang, Mike, Hermoso, Miquel Jubert, Metanat, Mo, Rastegari, Mohammad, Bansal, Munish, Santhanam, Nandhini, Parks, Natascha, White, Natasha, Bawa, Navyata, Singhal, Nayan, Egebo, Nick, Usunier, Nicolas, Mehta, Nikhil, Laptev, Nikolay Pavlovich, Dong, Ning, Cheng, Norman, Chernoguz, Oleg, Hart, Olivia, Salpekar, Omkar, Kalinli, Ozlem, Kent, Parkin, Parekh, Parth, Saab, Paul, Balaji, Pavan, Rittner, Pedro, Bontrager, Philip, Roux, Pierre, Dollar, Piotr, Zvyagina, Polina, Ratanchandani, Prashant, Yuvraj, Pritish, Liang, Qian, Alao, Rachad, Rodriguez, Rachel, Ayub, Rafi, Murthy, Raghotham, Nayani, Raghu, Mitra, Rahul, Parthasarathy, Rangaprabhu, Li, Raymond, Hogan, Rebekkah, Battey, Robin, Wang, Rocky, Howes, Russ, Rinott, Ruty, Mehta, Sachin, Siby, Sachin, Bondu, Sai Jayesh, Datta, Samyak, Chugh, Sara, Hunt, Sara, Dhillon, Sargun, Sidorov, Sasha, Pan, Satadru, Mahajan, Saurabh, Verma, Saurabh, Yamamoto, Seiji, Ramaswamy, Sharadh, Lindsay, Shaun, Lindsay, Shaun, Feng, Sheng, Lin, Shenghao, Zha, Shengxin Cindy, Patil, Shishir, Shankar, Shiva, Zhang, Shuqiang, Zhang, Shuqiang, Wang, Sinong, Agarwal, Sneha, Sajuyigbe, Soji, Chintala, Soumith, Max, Stephanie, Chen, Stephen, Kehoe, Steve, Satterfield, Steve, Govindaprasad, Sudarshan, Gupta, Sumit, Deng, Summer, Cho, Sungmin, Virk, Sunny, Subramanian, Suraj, Choudhury, Sy, Goldman, Sydney, Remez, Tal, Glaser, Tamar, Best, Tamara, Koehler, Thilo, Robinson, Thomas, Li, Tianhe, Zhang, Tianjun, Matthews, Tim, Chou, Timothy, Shaked, Tzook, Vontimitta, Varun, Ajayi, Victoria, Montanez, Victoria, Mohan, Vijai, Kumar, Vinay Satish, Mangla, Vishal, Ionescu, Vlad, Poenaru, Vlad, Mihailescu, Vlad Tiberiu, Ivanov, Vladimir, Li, Wei, Wang, Wenchen, Jiang, Wenwen, Bouaziz, Wes, Constable, Will, Tang, Xiaocheng, Wu, Xiaojian, Wang, Xiaolan, Wu, Xilun, Gao, Xinbo, Kleinman, Yaniv, Chen, Yanjun, Hu, Ye, Jia, Ye, Qi, Ye, Li, Yenda, Zhang, Yilin, Zhang, Ying, Adi, Yossi, Nam, Youngjin, Yu, Wang, Zhao, Yu, Hao, Yuchen, Qian, Yundi, Li, Yunlu, He, Yuzi, Rait, Zach, DeVito, Zachary, Rosnbrick, Zef, Wen, Zhaoduo, Yang, Zhenyu, Zhao, Zhiwei, Ma, Zhiyu

  12. CFBench: A Comprehensive Constraints-Following Benchmark for LLMs — published result table ↗

    Tao Zhang, Chenglin Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang et al. · 2024

    All 14 authors

    Tao Zhang, Chenglin Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

  13. LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs — published result table ↗

    Bai, Yushi, Zhang, Jiajie, Lv, Xin, Zheng, Linzhi, Zhu, Siqi, Hou, Lei et al. · 2024

    All 9 authors

    Bai, Yushi, Zhang, Jiajie, Lv, Xin, Zheng, Linzhi, Zhu, Siqi, Hou, Lei, Dong, Yuxiao, Tang, Jie, Li, Juanzi

  14. SysBench: Can Large Language Models Follow System Messages? — published result table ↗

    Yanzhao Qin, Tao Zhang, Tao Zhang, Yanjun Shen, Wenjing Luo, Haoze Sun et al. · 2024

    All 12 authors

    Yanzhao Qin, Tao Zhang, Tao Zhang, Yanjun Shen, Wenjing Luo, Haoze Sun, Yan Zhang, Yujing Qiao, Weipeng Chen, Zenan Zhou, Wentao Zhang, Bin Cui

  15. Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale — published result table ↗

    Bonatti, Rogerio, Zhao, Dan, Bonacci, Francesco, Dupont, Dillon, Abdali, Sara, Li, Yinheng et al. · 2024

    All 12 authors

    Bonatti, Rogerio, Zhao, Dan, Bonacci, Francesco, Dupont, Dillon, Abdali, Sara, Li, Yinheng, Lu, Yadong, Wagle, Justin, Koishida, Kazuhito, Bucker, Arthur, Jang, Lawrence, Hui, Zack

  16. CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark — published result table ↗

    Zachary S. Siegel, Sayash Kapoor, Nitya Nadgir, Benedikt Stroebl, Arvind Narayanan · 2024

  17. Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries — published result table ↗

    Vodrahalli, Kiran, Ontanon, Santiago, Tripuraneni, Nilesh, Xu, Kelvin, Jain, Sanil, Shivanna, Rakesh et al. · 2024

    All 24 authors

    Vodrahalli, Kiran, Ontanon, Santiago, Tripuraneni, Nilesh, Xu, Kelvin, Jain, Sanil, Shivanna, Rakesh, Hui, Jeffrey, Dikkala, Nishanth, Kazemi, Mehran, Fatemi, Bahare, Anil, Rohan, Dyer, Ethan, Shakeri, Siamak, Vij, Roopali, Mehta, Harsh, Ramasesh, Vinay, Le, Quoc, Chi, Ed, Lu, Yifeng, Firat, Orhan, Lazaridou, Angeliki, Lespiau, Jean-Baptiste, Attaluri, Nithya, Olszewska, Kate

  18. SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? — published result table ↗

    John Yang, Carlos E. Jimenez, Alex L. Zhang, Kilian Lieret, Joyce Yang, Xindi Wu et al. · 2024

    All 13 authors

    John Yang, Carlos E. Jimenez, Alex L. Zhang, Kilian Lieret, Joyce Yang, Xindi Wu, Ori Press, Niklas Muennighoff, Gabriel Synnaeve, Karthik R. Narasimhan, Diyi Yang, Sida I. Wang, Ofir Press

  19. ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery — published result table ↗

    Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu et al. · 2024

    All 20 authors

    Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, Huan Sun

  20. JudgeBench: A Benchmark for Evaluating LLM-based Judges — published result table ↗

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang et al. · 2024

    All 8 authors

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, Ion Stoica

  21. HARP: A challenging human-annotated math reasoning benchmark — published result table ↗

    Yue, Albert S., Madaan, Lovish, Moskovitz, Ted, Strouse, DJ, Singh, Aaditya K. · 2024

  22. 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

  23. PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models — published result table ↗

    Song, Mingyang, Su, Zhaochen, Qu, Xiaoye, Zhou, Jiawei, Cheng, Yu · 2025

  24. The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input — published result table ↗

    Alon Jacovi, Andrew Wang, Chris Alberti, Connie Tao, Jon Lipovetz, Kate Olszewska et al. · 2025

    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

  25. MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents — published result table ↗

    Jiang, Yixing, Black, Kameron C., Geng, Gloria, Park, Danny, Zou, James, Ng, Andrew Y. et al. · 2025

    All 7 authors

    Jiang, Yixing, Black, Kameron C., Geng, Gloria, Park, Danny, Zou, James, Ng, Andrew Y., Chen, Jonathan H.

  26. MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding — published result table ↗

    Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua et al. · 2025

    All 9 authors

    Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, Bowen Zhou

  27. ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning — published result table ↗

    Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark et al. · 2025

    All 7 authors

    Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi

  28. KernelBench: Can LLMs Write Efficient GPU Kernels? — published result table ↗

    Ouyang, Anne, Guo, Simon, Arora, Simran, Zhang, Alex L., Hu, William, Ré, Christopher et al. · 2025

    All 7 authors

    Ouyang, Anne, Guo, Simon, Arora, Simran, Zhang, Alex L., Hu, William, Ré, Christopher, Mirhoseini, Azalia

  29. SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? — published result table ↗

    Miserendino, Samuel, Wang, Michele, Patwardhan, Tejal, Heidecke, Johannes · 2025

  30. BIG-Bench Extra Hard — published result table ↗

    Kazemi, Mehran, Fatemi, Bahare, Bansal, Hritik, Palowitch, John, Anastasiou, Chrysovalantis, Mehta, Sanket Vaibhav et al. · 2025

    All 20 authors

    Kazemi, Mehran, Fatemi, Bahare, Bansal, Hritik, Palowitch, John, Anastasiou, Chrysovalantis, Mehta, Sanket Vaibhav, Jain, Lalit K., Aglietti, Virginia, Jindal, Disha, Chen, Peter, Dikkala, Nishanth, Tyen, Gladys, Liu, Xin, Shalit, Uri, Chiappa, Silvia, Olszewska, Kate, Tay, Yi, Tran, Vinh Q., Le, Quoc V., Firat, Orhan

  31. PaperBench: Evaluating AI's Ability to Replicate AI Research — published result table ↗

    Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin et al. · 2025

    All 13 authors

    Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, Tejal Patwardhan

  32. Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving — published result table ↗

    Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin et al. · 2025

    All 19 authors

    Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin, Lu Chen, Qi Liu, Xiaojian Zhong, Aoyan Li, Siyao Liu, Yongsheng Xiao, Liangqiang Chen, Yuyu Zhang, Jing Su, Tianyu Liu, Rui Long, Kai Shen, Liang Xiang

  33. HalluLens: LLM Hallucination Benchmark — published result table ↗

    Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang et al. · 2025

    All 8 authors

    Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung

  34. SWE-bench Goes Live! — published result table ↗

    Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie et al. · 2025

    All 15 authors

    Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang

  35. LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? — published result table ↗

    Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He et al. · 2025

    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

  36. Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs — published result table ↗

    Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno et al. · 2025

    All 8 authors

    Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno, Obbad, Elyas, Koyejo, Sanmi

  37. CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning — published result table ↗

    Deason, Lauren, Bali, Adam, Bejean, Ciprian, Bolocan, Diana, Crnkovich, James, Croitoru, Ioana et al. · 2025

    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

  38. CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers — published result table ↗

    Pan, Haining, Roggeveen, James V., Berg, Erez, Carrasquilla, Juan, Chowdhury, Debanjan, Ganguli, Surya et al. · 2025

    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

  39. Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments — published result table ↗

    Froger, Romain, Andrews, Pierre, Bettini, Matteo, Budhiraja, Amar, Cabral, Ricardo Silveira, Do, Virginie et al. · 2026

    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

  40. 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

  41. DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data — published result table ↗

    Esben Kran, Hieu Minh Nguyen, Akash Kundu, Sami Jawhar, Jinsuk Park, Mateusz Maria Jurewicz · 2025

  42. EconBench: Economic choice by language models — gpt-4o prototype metrics ↗

    EconBench project · 2026

  43. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  44. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  45. NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

    Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner et al. · 2024

    All 13 authors

    Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner, Boyuan Chen, Max Yin, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Muhammad Shafique

  46. Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models — published result table ↗

    Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu et al. · 2024

    All 20 authors

    Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu, Li, Lei, Ma, Chenghao, Chen, Liang, Xu, Runxin, Tang, Zhengyang, Wang, Benyou, Zan, Daoguang, Quan, Shanghaoran, Zhang, Ge, Sha, Lei, Zhang, Yichang, Ren, Xuancheng, Liu, Tianyu, Chang, Baobao

  47. OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems — published result table ↗

    Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen et al. · 2024

    All 14 authors

    Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun

  48. 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

  49. BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese — published result table ↗

    Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye et al. · 2025

    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

  50. 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

  51. PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change — published result table ↗

    Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati · 2022

  52. AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models — published result table ↗

    Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang et al. · 2023

    All 9 authors

    Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan

  53. BLINK: Multimodal Large Language Models Can See but Not Perceive — published result table ↗

    Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin et al. · 2024

    All 10 authors

    Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, Ranjay Krishna

  54. BioLP-bench: Measuring understanding of biological lab protocols by large language models ↗

    Igor Ivanov · 2024

Names used in source tables

GPT-4o · GPT-4o 🥉 · GPT-4o† · GPT-4o∗ · GPT4O · GPT4o · GPT4o† · gpt-4o · 🥈 GPT-4o