GPT-4o · Scale Labs · Risk elicitation score ↗
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
gpt-4o · Scale Labs · Score ↗
GPT-4o · SWE-bench Live · Resolved tasks · Lite comparison ↗
2 more reported results
10% · GPT-4o · SWE-bench Live · Resolved tasks · Lite comparison · SWE-bench Goes Live! ↗
11.67% · GPT-4o · SWE-bench Live · Resolved tasks · Lite comparison · SWE-bench Goes Live! ↗
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 ↗
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 ↗
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? ↗
GPT-4o · OlympiadBench (Math) · Mathematics · Full multimodal benchmark ↗
1 more reported result
41.54% · GPT-4o · OlympiadBench (Math) · Mathematics · Text-only problems · OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems ↗
GPT-4o · OlympiadBench · physics · Full multimodal benchmark ↗
1 more reported result
27.64% · GPT-4o · OlympiadBench · physics · Text-only problems · OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems ↗
GPT4o · BrowseComp-ZH · Answer accuracy · original comparison ↗
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 ↗
🥈 GPT-4o · BLINK · Test accuracy · 1,907 test items ↗
GPT4o · MMT-Bench & MuirBench · MMT-Bench accuracy · primary leaderboard ↗
GPT-4o 🥉 · PhysBench · Overall accuracy · physical-world question answering ↗
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 ↗
GPT-4o · ZebraLogic · Grid-level accuracy · complete puzzles ↗
GPT-4o · PlanBench · Blocksworld ↗
1 more reported result
0% · GPT-4o · PlanBench · Mystery Blocksworld · PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change ↗
GPT-4o · HalluLens · PreciseWikiQA correct answers · three-trial mean ↗
1 more reported result
65.89% · GPT-4o · HalluLens · MixedEntities false acceptance · fabricated-entity acceptance · HalluLens: LLM Hallucination Benchmark ↗
GPT-4o · MedXpertQA (Text & MM) · Multimodal-model text subset ↗
1 more reported result
42.8% · GPT-4o · MedXpertQA (Text & MM) · Multimodal subset · MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding ↗
GPT-4o · AgentHarm & AgentDojo · AgentDojo targeted attack success · agent comparison ↗
1 more reported result
69% · GPT-4o · AgentHarm & AgentDojo · AgentDojo benign utility · agent comparison · AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents ↗
GPT-4o · HumanEval+ · Pass@1 · Llama 3 report · post-trained models ↗
GPT-4o · HumanEval · Pass@1 · Llama 3 report · post-trained models ↗
GPT-4o · DocVQA · Test ANLS · Llama 3 vision report · published comparison ↗
GPT-4o · ChartQA · Test accuracy · chain of thought · Llama 3 vision report · published comparison ↗
GPT-4o · Omni-MATH · Overall accuracy · official leaderboard ↗
1 more reported result
29.2% · GPT-4o · Omni-MATH · Rule-based accuracy · rule-based evaluation · Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models ↗
gpt-4o · PaperBench · Average replication score · BasicAgent ↗
GPT-4o · VisualWebArena · Overall task success · updated model comparison ↗
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 ↗
GPT-4o · JudgeBench · Overall pairwise accuracy · Arena-Hard judge scaffold ↗
GPT-4o · WorkArena & WorkArena++ · WorkArena L1 · success rate · BrowserGym · tuned agent configurations ↗
GPT4o · BIG-Bench Extra Hard (BBEH) · Overall accuracy · original model comparison ↗
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 ↗
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 ↗
GPT-4o · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
72% · GPT-4o · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
GPT-4o · PRMBench · Overall PRMScore · original evaluation ↗
GPT-4o · MedAgentBench · Overall task success · original evaluation ↗
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? ↗
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 ↗
GPT4o† · SysBench / RealGuardrails · instruction satisfaction rate · English-oriented models ↗
GPT-4o · FACTS Grounding · Final factuality score · judge average · eligibility-filtered evaluation ↗
GPT-4o · MixEval & MixEval-Hard · accuracy · original comparison ↗
1 more reported result
87.9% · GPT-4o · MixEval & MixEval-Hard · accuracy · original comparison · MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures ↗
GPT-4o · WildHallucinations · WildFActScore · all entities · original evaluation ↗
GPT-4o · KernelBench · Level 1 · fast_1 vs torch.compile default · NVIDIA L40S comparison ↗
2 more reported results
4% · GPT-4o · KernelBench · Level 2 · fast_1 vs torch.compile default · NVIDIA L40S comparison · KernelBench: Can LLMs Write Efficient GPU Kernels? ↗
4% · GPT-4o · KernelBench · Level 3 · fast_1 vs torch.compile default · NVIDIA L40S comparison · KernelBench: Can LLMs Write Efficient GPU Kernels? ↗
GPT-4o · HARP · Human Annotated Reasoning Problems · Short-answer overall accuracy · zero-shot chain of thought ↗
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 ↗
GPT-4o∗ · HelloBench / LongWriter-6k · LongBench-Write · overall score · LongWriter report ↗
GPT-4o · CMT-Benchmark v1.0.0 · Overall pass@1 · expert condensed-matter problems ↗
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? ↗
GPT-4o · SpatialBench / VSI-Bench · overall average · original full-set evaluation ↗
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? ↗
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 ↗
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 ↗
GPT-4o · BioLP-Bench · rounded reported accuracy · Figure 1 caption · three-run means ↗
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 ↗
gpt-4o · EconBench · EconBench prototype · risk-consistency error · published prototype observations ↗
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 ↗
GPT-4o · Michelangelo (Latent Structure Queries) · MRCR · cumulative mean through 128K · chart reading · Michelangelo original report · MRCR at 128K context ↗
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 ↗
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.
- VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks — published result table ↗
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
- WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? — published result table ↗
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
- MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗
All 8 authors
Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains — published result table ↗
- Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? — published result table ↗
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
- AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents — published result table ↗
- LAB-Bench: Measuring Capabilities of Language Models for Biology Research — published result table ↗
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
- The Art of Saying No: Contextual Noncompliance in Language Models — published result table ↗
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
- 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
- AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents — published result table ↗
All 9 authors
Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, Niranjan Balasubramanian
- The Llama 3 Herd of Models — published result table ↗
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
- CFBench: A Comprehensive Constraints-Following Benchmark for LLMs — published result table ↗
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
- LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs — published result table ↗
All 9 authors
Bai, Yushi, Zhang, Jiajie, Lv, Xin, Zheng, Linzhi, Zhu, Siqi, Hou, Lei, Dong, Yuxiao, Tang, Jie, Li, Juanzi
- SysBench: Can Large Language Models Follow System Messages? — published result table ↗
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
- Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale — published result table ↗
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
- CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark — published result table ↗
- Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries — published result table ↗
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
- SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? — published result table ↗
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
- ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery — published result table ↗
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
- 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 ↗
- PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models — 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
- MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents — published result table ↗
All 7 authors
Jiang, Yixing, Black, Kameron C., Geng, Gloria, Park, Danny, Zou, James, Ng, Andrew Y., Chen, Jonathan H.
- MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding — published result table ↗
All 9 authors
Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, Bowen Zhou
- 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
- KernelBench: Can LLMs Write Efficient GPU Kernels? — published result table ↗
All 7 authors
Ouyang, Anne, Guo, Simon, Arora, Simran, Zhang, Alex L., Hu, William, Ré, Christopher, Mirhoseini, Azalia
- SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? — published result table ↗
- BIG-Bench Extra Hard — published result table ↗
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
- PaperBench: Evaluating AI's Ability to Replicate AI Research — published result table ↗
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
- Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving — published result table ↗
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
- HalluLens: LLM Hallucination Benchmark — published result table ↗
All 8 authors
Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
- SWE-bench Goes Live! — published result table ↗
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
- 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
- Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs — published result table ↗
All 8 authors
Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno, Obbad, Elyas, Koyejo, Sanmi
- 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
- Are large language models superhuman chemists? — published result table ↗
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
- DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data — published result table ↗
- EconBench: Economic choice by language models — gpt-4o prototype metrics ↗
- Scale Labs — evaluation methodology and results ↗
- Scale Labs — evaluation methodology and results ↗
- NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
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
- Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models — published result table ↗
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
- OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems — published result table ↗
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
- MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI — published result table ↗
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
- 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
- PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding — published result table ↗
- PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change — published result table ↗
- AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models — published result table ↗
All 9 authors
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
- BLINK: Multimodal Large Language Models Can See but Not Perceive — published result table ↗
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
- BioLP-bench: Measuring understanding of biological lab protocols by large language models ↗
Names used in source tables
GPT-4o · GPT-4o 🥉 · GPT-4o† · GPT-4o∗ · GPT4O · GPT4o · GPT4o† · gpt-4o · 🥈 GPT-4o