Benchmark By Score Rank
Overall 1
AA Intelligence Index4.3.2 AA Intelligence Index 4.3.2: Artificial Analysis combines tests of knowledge, reasoning, coding and tool use into one score AA 22.8 #98 of 337
Knowledge 4
AA OmniscienceAccuracy AA Omniscience · Accuracy: Measures correct answers on the AA Omniscience factual knowledge test AA 26.8% #98 of 321+1 versions & slices Hallucination Rate AA Omniscience · Hallucination Rate: Measures how often attempted answers are incorrect on the AA Omniscience factual knowledge test AA 35.6% #51 of 321
AA Omniscience Index AA Omniscience Index: Scores factual knowledge while penalizing incorrect answers to encourage admitting uncertainty AA 0.8 #67 of 321
IFBench (instructions) IFBench (instructions): Checks whether the model follows precise formatting and content instructions AA 75.7% #19 of 248
Math 1
AA Analyst Agent AA Analyst Agent: Data-analyst tasks: the model explores data with tools and reports findings AA 11.3% #38 of 43
Science 3
Humanity's Last Exam Humanity's Last Exam: Very hard expert questions across many fields, written to stump today’s models AA 29.6% #82 of 337
CritPt (physics) CritPt (physics): Research-level physics problems that need many careful reasoning steps AA 0.6% #141 of 324
SciCode SciCode: Writing code for real scientific computing problems (physics, biology, math) AA 50.1% #50 of 140
Coding 8
Terminal-Bench4.0 Terminal-Bench 4.0 · source setting 2: Agents use a terminal to complete executable coding, system administration and research tasks in the 4.0 suite 0% #75 of 132+1 versions & slices 2.1 Terminal-Bench 2.1 · source setting 2: Older, easier version of Terminal-Bench; Still widely reported AA 55.4% #60 of 156
DeepSWE1.1 DeepSWE 1.1: Agents change repository code to resolve software engineering tasks under the stated DeepSWE edition 0% #41 of 44+1 versions & slices 1 DeepSWE 1: Agents change repository code to resolve software engineering tasks under the stated DeepSWE edition Datacurve 0.2% #19 of 20
ITBench SRE ITBench SRE: IT incident tasks: diagnose and fix broken systems like a site-reliability engineer AA 26.5% #32 of 37
WebDev Arena WebDev Arena: Human pairwise preferences in the stated webdev vote pool Arena 1,399 #70 of 97+1 versions & slices full cohort WebDev Arena · snapshot 2026-10-19 · native board settings: Human pairwise preferences in the stated webdev vote pool Arena 1,399 #70 of 97
Terminal-Bench Hard Terminal-Bench Hard (version unstated): Agents solve difficult software, system-administration and data-processing tasks in a terminal AA 39.4% #34 of 242
Agents 5
AutomationBench-AA AutomationBench-AA: Agents orchestrate SaaS workflows through APIs; objectives receive credit only when the task has no guardrail violation AA 4.4% #88 of 133
AA-Briefcase1.1 · Elo AA-Briefcase 1.1 · Elo: Agents produce professional deliverables; blind pairwise comparisons produce the source Elo rating AA 719 #83 of 140
APEX-Agents APEX-Agents: Agents complete long-horizon professional-services tasks across realistic applications AA 10.6% #25 of 29
GDPval-AA (work tasks)2.1 GDPval-AA (work tasks) 2.1: Real work tasks from many professions (reports, spreadsheets, analyses), graded against expert work AA 25.5 #78 of 175
τ³-BankingAA τ³-Banking · AA: Agents retrieve banking knowledge and execute tools to resolve customer-support workflows AA 9.9% #90 of 134
Writing 16
Arena Textcreative writing Arena Text · creative writing: Human pairwise preferences in the stated text vote pool Arena 1,364 #94 of 195+14 versions & slices overall Arena Text · overall: Human pairwise preferences in the stated text vote pool Arena 1,415 #86 of 196Chinese Arena Text · chinese: Human pairwise preferences in the stated text vote pool Arena 1,441 #89 of 181English Arena Text · english: Human pairwise preferences in the stated text vote pool Arena 1,426 #77 of 188French Arena Text · french: Human pairwise preferences in the stated text vote pool Arena 1,423 #77 of 149German Arena Text · german: Human pairwise preferences in the stated text vote pool Arena 1,399 #72 of 154instruction following Arena Text · instruction following: Human pairwise preferences in the stated text vote pool Arena 1,405 #76 of 188Japanese Arena Text · japanese: Human pairwise preferences in the stated text vote pool Arena 1,261 #98 of 138Korean Arena Text · korean: Human pairwise preferences in the stated text vote pool Arena 1,314 #89 of 144non-English Arena Text · non english: Human pairwise preferences in the stated text vote pool Arena 1,382 #94 of 188Polish Arena Text · polish: Human pairwise preferences in the stated text vote pool Arena 1,388 #76 of 133Russian Arena Text · russian: Human pairwise preferences in the stated text vote pool Arena 1,383 #92 of 186Spanish Arena Text · spanish: Human pairwise preferences in the stated text vote pool Arena 1,402 #86 of 155overall · full cohort Arena Text · overall · snapshot 2026-10-15 · native board settings: Human pairwise preferences in the stated text vote pool Arena 1,405 #88 of 188creative writing · full cohort Arena Text · creative writing · snapshot 2026-10-15 · native board settings: Human pairwise preferences in the stated text vote pool Arena 1,354 #92 of 187
Lech Mazur Short-Story Lech Mazur Short-Story · comparison score: Judges compare stories written to ten required elements; the comparison score can be negative Lech -3.8 #42 of 44
Health & law 5
MLCR-AA MLCR-AA: Models synthesize long medical records; the headline credits concise answers judged both accurate and complete AA 3.3% #53 of 64
Vals LegalBench Vals LegalBench: Legal reasoning and legal-language understanding on LegalBench Vals 84% #39 of 89
Vals MedCode Vals MedCode: Medical coding accuracy on patient encounters Vals 34.4% #60 of 78
Vals MedScribe Vals MedScribe: Accuracy of clinical notes produced from consultation transcripts Vals 79.9% #50 of 80
Vals Legal Research Bench Vals Legal Research Bench: Grounded legal research; a task passes only if all criteria pass Vals 10.6% #60 of 69
Documents 2
GDP.pdfAA all-pass GDP.pdf · AA all-pass: Models reason over professional PDF documents; an attempt passes only when every atomic criterion passes 6% #78 of 133
AA-LCR (long context)1.1 AA-LCR (long context) 1.1: Questions that need reading and connecting facts across very long documents (around 100K tokens) AA 78.3% #61 of 275
Legacy · saturated or no longer reported 8
GPQA Diamond GPQA Diamond: Graduate-level biology, chemistry and physics questions that can’t be solved by a quick web search 87.4% #54 of 315
SWE-rebenchall · 2026-02-01 to 2026-03-01 SWE-rebench · all · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 51.9% #5 of 5+5 versions & slices Go · 2026-02-01 to 2026-03-01 SWE-rebench · go · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 5Java · 2026-02-01 to 2026-03-01 SWE-rebench · java · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 5Python · 2026-02-01 to 2026-03-01 SWE-rebench · python · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 51.9% #5 of 5Rust · 2026-02-01 to 2026-03-01 SWE-rebench · rust · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 5TypeScript · 2026-02-01 to 2026-03-01 SWE-rebench · typescript · 2026-02-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 5
τ²-benchAA τ²-Bench: Tests service agents that use tools while coordinating with a simulated user AA 84.8% #65 of 245
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