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 19.3 #123 of 337
Knowledge 6
AA OmniscienceAccuracy AA Omniscience · Accuracy: Measures correct answers on the AA Omniscience factual knowledge test AA 28.4% #86 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 52.7% #96 of 321
AA Omniscience Index AA Omniscience Index: Scores factual knowledge while penalizing incorrect answers to encourage admitting uncertainty AA -9.4 #90 of 321
IFBench (instructions) IFBench (instructions): Checks whether the model follows precise formatting and content instructions AA 42.7% #143 of 248
EQ-Bench3 EQ-Bench 3: Judge-rated responses to social and emotionally nuanced scenarios, scored through the source pairwise ranking EQ-Bench 1,511 #20 of 50
SimpleQA Verifiedv1 SimpleQA Verified v1: Accuracy on short factual questions with verified answers, answered without web access 29.2% #59 of 83
Reasoning 5
ARC-AGI-1public evaluation ARC-AGI-1 · public-eval: Models infer transformations from examples and solve unseen grid puzzles; the evaluation split is explicit ARC 73.8% #40 of 61+1 versions & slices semi-private ARC-AGI-1 · semi-private: Models infer transformations from examples and solve unseen grid puzzles; the evaluation split is explicit ARC 63.7% #43 of 64
ARC-AGI-2public evaluation ARC-AGI-2 · public-eval: Models infer transformations from examples and solve unseen grid puzzles; the evaluation split is explicit ARC 14.7% #39 of 63+1 versions & slices semi-private ARC-AGI-2 · semi-private: Models infer transformations from examples and solve unseen grid puzzles; the evaluation split is explicit ARC 13.6% #43 of 66
Chess Puzzles Chess Puzzles: Models choose the best next move from a novel chess position Epoch 6% #63 of 68
Math 14
FrontierMath FrontierMath: Models solve expert-written advanced mathematics problems on the named tier and edition Epoch 9.3% #26 of 40
FrontierMath Tier4 FrontierMath Tier 4: Models solve expert-written advanced mathematics problems on the named tier and edition Epoch 2.1% #23 of 33
MathArenaAIME 2025 MathArena · aime · AIME 2025: Answer accuracy on the named mathematics competition under the recorded effort MathArena 84.2% #19 of 30+11 versions & slices Apex MathArena · apex · Apex: Answer accuracy on the named mathematics competition under the recorded effort MathArena 1.6% #23 of 34BRUMO 2025 MathArena · brumo · BRUMO 2025: Answer accuracy on the named mathematics competition under the recorded effort MathArena 90.8% #13 of 24CMIMC 2025 MathArena · cmimc · CMIMC 2025: Answer accuracy on the named mathematics competition under the recorded effort MathArena 66.3% #19 of 22HMMT February 2025 MathArena · hmmt · HMMT Feb 2025: Answer accuracy on the named mathematics competition under the recorded effort MathArena 67.5% #21 of 30Kangaroo 2025 1-2 MathArena · kangaroo · Kangaroo 2025 1-2: Answer accuracy on the named mathematics competition under the recorded effort MathArena 61.5% #15 of 17Kangaroo 2025 11-12 MathArena · kangaroo · Kangaroo 2025 11-12: Answer accuracy on the named mathematics competition under the recorded effort MathArena 87.5% #14 of 17Kangaroo 2025 3-4 MathArena · kangaroo · Kangaroo 2025 3-4: Answer accuracy on the named mathematics competition under the recorded effort MathArena 62.5% #13 of 17Kangaroo 2025 5-6 MathArena · kangaroo · Kangaroo 2025 5-6: Answer accuracy on the named mathematics competition under the recorded effort MathArena 68.3% #11 of 17Kangaroo 2025 7-8 MathArena · kangaroo · Kangaroo 2025 7-8: Answer accuracy on the named mathematics competition under the recorded effort MathArena 80% #15 of 16Kangaroo 2025 9-10 MathArena · kangaroo · Kangaroo 2025 9-10: Answer accuracy on the named mathematics competition under the recorded effort MathArena 95% #11 of 16SMT 2025 MathArena · smt · SMT 2025: Answer accuracy on the named mathematics competition under the recorded effort MathArena 84% #15 of 24
Science 3
Humanity's Last Exam Humanity's Last Exam: Very hard expert questions across many fields, written to stump today’s models AA 7.2% #222 of 337
CritPt (physics) CritPt (physics): Research-level physics problems that need many careful reasoning steps AA 0% #179 of 324
SciCode SciCode: Writing code for real scientific computing problems (physics, biology, math) AA 45.7% #70 of 140
Coding 5
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 55.8% #59 of 156
WebDev Arena WebDev Arena: Human pairwise preferences in the stated webdev vote pool Arena 1,394 #73 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 1,394 #73 of 97
Terminal-Bench Hard Terminal-Bench Hard (version unstated): Agents solve difficult software, system-administration and data-processing tasks in a terminal AA 28.8% #61 of 242
Agents 8
AutomationBench-AA AutomationBench-AA: Agents orchestrate SaaS workflows through APIs; objectives receive credit only when the task has no guardrail violation AA 14.1% #70 of 133
AA-Briefcase1.1 · Elo AA-Briefcase 1.1 · Elo: Agents produce professional deliverables; blind pairwise comparisons produce the source Elo rating AA 713 #84 of 140
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 20.5 #91 of 175
MCP-Atlas Public MCPAtlas Public: MCPAtlas Public as reported in this launch evaluation; the post is the authority for the recorded task and grading scope 62% #11 of 16
τ³-benchbanking · 1.0.1 τ-bench official · 3-banking-1.0.1: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools Sierra 25.3% #16 of 21
EBR Bench EBR Bench: Agents learn to play Earthborne Rangers across repeated playthroughs Epoch 2.4% #23 of 24
MCP-Atlasfull 1000 MCP-Atlas · full 1000: Tool orchestration across MCP servers on the stated task split Scale 59.5% #11 of 16
τ³-BankingAA τ³-Banking · AA: Agents retrieve banking knowledge and execute tools to resolve customer-support workflows AA 24.5% #43 of 134
Multimodal 2
MMMU-Pro (vision) MMMU-Pro (vision): College-level questions that require understanding images: charts, diagrams, photos AA 65.2% #81 of 127
MMMUvalidation MMMU · validation: Image-grounded university-level questions across 30 disciplines; validation and test sets remain separate MMMU 77.8% #8 of 41
Writing 17
Arena Textcreative writing Arena Text · creative writing: Human pairwise preferences in the stated text vote pool Arena 1,454 #21 of 195+14 versions & slices overall Arena Text · overall: Human pairwise preferences in the stated text vote pool Arena 1,456 #43 of 196Chinese Arena Text · chinese: Human pairwise preferences in the stated text vote pool Arena 1,460 #73 of 181English Arena Text · english: Human pairwise preferences in the stated text vote pool Arena 1,450 #49 of 188French Arena Text · french: Human pairwise preferences in the stated text vote pool Arena 1,458 #43 of 149German Arena Text · german: Human pairwise preferences in the stated text vote pool Arena 1,421 #56 of 154instruction following Arena Text · instruction following: Human pairwise preferences in the stated text vote pool Arena 1,459 #30 of 188Japanese Arena Text · japanese: Human pairwise preferences in the stated text vote pool Arena 1,390 #54 of 138Korean Arena Text · korean: Human pairwise preferences in the stated text vote pool Arena 1,402 #40 of 144non-English Arena Text · non english: Human pairwise preferences in the stated text vote pool Arena 1,425 #55 of 188Polish Arena Text · polish: Human pairwise preferences in the stated text vote pool Arena 1,419 #58 of 133Russian Arena Text · russian: Human pairwise preferences in the stated text vote pool Arena 1,436 #45 of 186Spanish Arena Text · spanish: Human pairwise preferences in the stated text vote pool Arena 1,458 #30 of 155overall · full cohort Arena Text · overall · snapshot 2026-10-15 · native board settings: Human pairwise preferences in the stated text vote pool Arena 1,438 #52 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 1,442 #29 of 187
EQ-Bench Longform Creative Writing1.11 Longform Creative Writing 1.11: Models plan, revise and write a story across eight turns, graded with a writing rubric EQ-Bench 71.4% #36 of 95
EQ-Bench Creative Writingv3 Creative Writing v3 · Elo: Judge-rated creative writing under the v3 prompt set and pairwise ranking EQ-Bench 1,678 #33 of 91
Health & law 3
Vals LegalBench Vals LegalBench: Legal reasoning and legal-language understanding on LegalBench Vals 84.1% #34 of 89
Vals MedCode Vals MedCode: Medical coding accuracy on patient encounters Vals 44.1% #34 of 78
Vals MedScribe Vals MedScribe: Accuracy of clinical notes produced from consultation transcripts Vals 84.1% #34 of 80
Documents 3
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 54% #169 of 275
GDP.pdfAA all-pass GDP.pdf · AA all-pass: Models reason over professional PDF documents; an attempt passes only when every atomic criterion passes 5.2% #85 of 133
Arena Document Arena Document · overall: Human pairwise preferences in the stated document vote pool Arena 1,450 #22 of 33
Legacy · saturated or no longer reported 24
AIME2025 · AA archived AIME 2025 · AA archived: AA evaluates the 30 integer-answer problems from the 2025 AIME mathematics competition AA 37% #99 of 140
LiveCodeBenchAA archived LiveCodeBench · AA archived: AA evaluates solutions to newly collected competitive-programming problems AA 59% #75 of 153
GPQA Diamond GPQA Diamond: Graduate-level biology, chemistry and physics questions that can’t be solved by a quick web search 72.7% #170 of 315+1 versions & slices Epoch GPQA Diamond · source setting 2: Models answer graduate-level science multiple-choice questions Epoch 73.7% #76 of 133
BFCLv4 BFCL v4: Tests function calling, multi-turn interactions, search and memory in the v4 suite Berkeley 73.2% #2 of 39+1 versions & slices v4 · prompt mode BFCL v4 · BFCL v4 source function-call format · historical run: Tests function calling, multi-turn interactions, search and memory in the v4 suite Berkeley 24.9% #15 of 16
SWE-bench Pro (public) SWE-bench Pro (public): Agents resolve long-horizon professional repository tasks; public and private splits are separate 43.6% #7 of 19
τ²-benchairline · 0.1.3 τ-bench official · 2-airline-0.1.3: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools 70% #1 of 8+8 versions & slices airline · 1.0.1 τ-bench official · 2-airline-1.0.1: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools Sierra 72% #6 of 6overall · 0.1.3 τ-bench official · 2-overall-0.1.3: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools 84.7% #1 of 7overall · 1.0.1 τ-bench official · 2-overall-1.0.1: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools Sierra 76.4% #6 of 6retail · 0.1.3 τ-bench official · 2-retail-0.1.3: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools 86.2% #1 of 8retail · 1.0.1 τ-bench official · 2-retail-1.0.1: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools Sierra 72.4% #6 of 6telecom · 0.1.3 τ-bench official · 2-telecom-0.1.3: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools 98% #2 of 7telecom · 1.0.1 τ-bench official · 2-telecom-1.0.1: Customer-service agents converse with simulated users, retrieve domain knowledge and change state through tools Sierra 84.9% #6 of 6AA τ²-Bench: Tests service agents that use tools while coordinating with a simulated user AA 70.5% #94 of 245
OTIS Mock AIME 20242025 OTIS Mock AIME 2024 2025: Models solve the stated OTIS mock AIME mathematics problems Epoch 35.6% #100 of 130
SWE-bench VerifiedEpoch Swe Bench Verified: Agents modify real repositories to resolve the stated Verified issues Epoch 71.3% #18 of 24
SWE-rebenchall · 2025-09-01 to 2026-03-01 SWE-rebench · all · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 52.4% #1 of 1+5 versions & slices Go · 2025-09-01 to 2026-03-01 SWE-rebench · go · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 1Java · 2025-09-01 to 2026-03-01 SWE-rebench · java · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 1Python · 2025-09-01 to 2026-03-01 SWE-rebench · python · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 52.4% #1 of 1Rust · 2025-09-01 to 2026-03-01 SWE-rebench · rust · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 1TypeScript · 2025-09-01 to 2026-03-01 SWE-rebench · typescript · 2025-09-01 to 2026-03-01: Repository issue resolution over the exact fresh pull-request window, under the source agent version Nebius 0% #1 of 1
No benchmark matches
CheaperGLM 5.2maxOpen+41%-64%$1.47No image input
BetterMiMo v2.5thinkingOpen+29%-97%$0.019
CheaperStep 3.7 FlashhighOpen+40%-93%$0.20 / $1.15Context: 1M → 256K
Solar Pro 3high+22%-96%$0.079Context: 1M → 131K+1aTrinity Large ThinkingthinkingOpen+28%-94%$0.12Context: 1M → 262K+2
GPT-5.4 Nanoxhigh+8%-92%$0.18Context: 1M → 400K