Rankings / OpenAI
O4 Mini (2025-04-16)
released 2025-04-16
BenchAtlas Index
as of 2026-07-14
high effort
rank #214
15 families · 5 categories · high
medium effort
rank #244
4 families · 3 categories · medium
high effort
rank #257
8 families · 5 categories · high
Benchmark evidence
92 results
agentic coding
| τ²-Bench | high effort | 55.6 % | independent | Artificial Analysis |
coding
| LiveCodeBench v6 implementation=artificial-analysis | high effort | 85.9 % | independent | Artificial Analysis |
| LiveCodeBench v6 problems=454 · window_start=2024-08-01 | high effort | 80.2 % | independent | LiveCodeBench Leaderboard |
| LiveCodeBench v5older version problems=279 · window_start=2024-08-01 | high effort | 74.5 % | independent | LiveCodeBench Leaderboard |
| LiveCodeBench v6 problems=454 · window_start=2024-08-01 | medium effort | 74.2 % | independent | LiveCodeBench Leaderboard |
| LiveCodeBench v6 problems=454 · window_start=2024-08-01 | low effort | 65.9 % | independent | LiveCodeBench Leaderboard |
| SciCode | high effort | 46.5 % | independent | Artificial Analysis |
external indices
| Intelligence Index v4.1 | high effort | 25.6 points | independent | Artificial Analysis |
| AA Math Index | high effort | 90.7 points | independent | Artificial Analysis |
| ECI | high effort | 146.5 points | independent | Epoch AI Benchmarking Hub |
| ECI | 146.5 points | independent | Epoch AI Benchmarking Hub | |
| ECI | low effort | 146.5 points | independent | Epoch AI Benchmarking Hub |
| ECI | medium effort | 146.5 points | independent | Epoch AI Benchmarking Hub |
factuality
| SimpleQA Verified | high effort | 23.9 % | independent | Epoch AI Benchmarking Hub 2025-12-09 |
human preference
| Coding (style control)older version arena=text · category=coding · style_control=true | 1432.1 1425.2–1439.0 | community | LMArena Leaderboard Dataset | |
| Industry Software And It Services (style control)older version arena=text · category=industry_software_and_it_services · style_control=true | 1421.2 1415.6–1426.9 | community | LMArena Leaderboard Dataset | |
| Hard Prompts English (style control)older version arena=text · category=hard_prompts_english · style_control=true | 1417.3 1411.0–1423.7 | community | LMArena Leaderboard Dataset | |
| Industry Medicine And Healthcare (style control)older version arena=text · category=industry_medicine_and_healthcare · style_control=true | 1417.1 1404.7–1429.5 | community | LMArena Leaderboard Dataset | |
| Math (style control)older version arena=text · category=math · style_control=true | 1415.6 1405.0–1426.3 | community | LMArena Leaderboard Dataset | |
| Industry Mathematical (style control)older version arena=text · category=industry_mathematical · style_control=true | 1409.5 1398.5–1420.4 | community | LMArena Leaderboard Dataset | |
| French (style control)older version arena=text · category=french · style_control=true | 1406.8 1381.1–1432.4 | community | LMArena Leaderboard Dataset | |
| Expert (style control)older version arena=text · category=expert · style_control=true | 1406.0 1393.5–1418.6 | community | LMArena Leaderboard Dataset | |
| Hard Prompts (style control)older version arena=text · category=hard_prompts · style_control=true | 1405.4 1400.2–1410.6 | community | LMArena Leaderboard Dataset | |
| Polish (style control)older version arena=text · category=polish · style_control=true | 1404.8 1392.6–1417.1 | community | LMArena Leaderboard Dataset | |
| Industry Life And Physical And Social Science (style control)older version arena=text · category=industry_life_and_physical_and_social_science · style_control=true | 1403.1 1395.8–1410.5 | community | LMArena Leaderboard Dataset | |
| English (style control)older version arena=text · category=english · style_control=true | 1403.1 1398.1–1408.0 | community | LMArena Leaderboard Dataset | |
| Chinese (style control)older version arena=text · category=chinese · style_control=true | 1400.2 1387.6–1412.8 | community | LMArena Leaderboard Dataset | |
| Industry Legal And Government (style control)older version arena=text · category=industry_legal_and_government · style_control=true | 1398.7 1387.2–1410.2 | community | LMArena Leaderboard Dataset | |
| Overall (style control) arena=text · category=overall · style_control=true | 1389.9 1385.9–1393.9 | community | LMArena Leaderboard Dataset | |
| Spanish (style control)older version arena=text · category=spanish · style_control=true | 1383.6 1363.0–1404.2 | community | LMArena Leaderboard Dataset | |
| Multi Turn (style control)older version arena=text · category=multi_turn · style_control=true | 1380.6 1373.4–1387.8 | community | LMArena Leaderboard Dataset | |
| German (style control)older version arena=text · category=german · style_control=true | 1379.0 1360.9–1397.2 | community | LMArena Leaderboard Dataset | |
| Industry Business And Management And Financial Operations (style control)older version arena=text · category=industry_business_and_management_and_financial_operations · style_control=true | 1378.5 1371.0–1386.0 | community | LMArena Leaderboard Dataset | |
| Non English (style control)older version arena=text · category=non_english · style_control=true | 1375.0 1370.0–1379.9 | community | LMArena Leaderboard Dataset | |
| Russian (style control)older version arena=text · category=russian · style_control=true | 1374.2 1363.5–1384.9 | community | LMArena Leaderboard Dataset | |
| Instruction Following (style control)older version arena=text · category=instruction_following · style_control=true | 1368.2 1362.1–1374.2 | community | LMArena Leaderboard Dataset | |
| Longer Query (style control)older version arena=text · category=longer_query · style_control=true | 1368.0 1361.1–1374.9 | community | LMArena Leaderboard Dataset | |
| Exclude Ties (style control)older version arena=text · category=exclude_ties · style_control=true | 1367.1 1361.7–1372.6 | community | LMArena Leaderboard Dataset | |
| Korean (style control)older version arena=text · category=korean · style_control=true | 1361.6 1341.3–1381.9 | community | LMArena Leaderboard Dataset | |
| Industry Writing And Literature And Language (style control)older version arena=text · category=industry_writing_and_literature_and_language · style_control=true | 1353.9 1347.4–1360.4 | community | LMArena Leaderboard Dataset | |
| Industry Entertainment And Sports And Media (style control)older version arena=text · category=industry_entertainment_and_sports_and_media · style_control=true | 1350.1 1343.0–1357.1 | community | LMArena Leaderboard Dataset | |
| Japanese (style control)older version arena=text · category=japanese · style_control=true | 1341.9 1321.9–1361.9 | community | LMArena Leaderboard Dataset | |
| Creative Writing (style control)older version arena=text · category=creative_writing · style_control=true | 1338.0 1329.8–1346.1 | community | LMArena Leaderboard Dataset |
knowledge science
| GPQA Diamond | high effort | 79.6 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
| GPQA Diamond implementation=artificial-analysis | high effort | 78.4 % | independent | Artificial Analysis |
| GPQA Diamond implementation=vals-ai | high effort | 74.5 % | independent | Vals AI |
| Humanity's Last Exam implementation=artificial-analysis | high effort | 17.5 % | independent | Artificial Analysis |
| MMLU-Pro implementation=artificial-analysis | high effort | 83.2 % | independent | Artificial Analysis |
| MMLU-Pro implementation=vals-ai | high effort | 80.6 % | independent | Vals AI |
long context instruction
| AA-LCR | high effort | 55.0 % | independent | Artificial Analysis |
| IFBench | high effort | 68.7 % | independent | Artificial Analysis |
multimodal
| MMMU implementation=vals-ai | high effort | 79.7 % | independent | Vals AI |
professional
| CorpFin | high effort | 59.0 % | independent | Vals AI |
| LegalBench | high effort | 79.2 % | independent | Vals AI |
| MedQA | high effort | 96.0 % | independent | Vals AI |
| TaxEval | high effort | 74.8 % | independent | Vals AI |
reasoning math
| AIME implementation=artificial-analysis | high effort | 94.0 % | independent | Artificial Analysis |
| AIME year=2025 · implementation=artificial-analysis | high effort | 90.7 % | independent | Artificial Analysis |
| AIME implementation=vals-ai | high effort | 83.7 % | independent | Vals AI |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort | 68.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 58.7 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort | 50.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort | 41.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort | 27.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort | 21.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 7.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 6.1 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 2.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 2.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort | 1.7 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 0.9 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 0.9 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort | 0.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort | 0.0 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort | 0.0 usd_per_task | independent | ARC Prize Leaderboard |
| FrontierMath | high effort | 24.8 % | independent | Epoch AI Benchmarking Hub 2025-11-13 |
| FrontierMath | medium effort | 19.0 % | independent | Epoch AI Benchmarking Hub 2025-11-13 |
| FrontierMath | low effort | 10.7 % | independent | Epoch AI Benchmarking Hub 2025-11-16 |
| FrontierMath Tier 4older version | high effort | 6.3 % | independent | Epoch AI Benchmarking Hub 2025-07-01 |
| FrontierMath Tier 4older version | medium effort | 2.1 % | independent | Epoch AI Benchmarking Hub 2025-08-07 |
| MATH-500 implementation=artificial-analysis | high effort | 98.9 % | independent | Artificial Analysis |
| MATH-500 implementation=vals-ai | high effort | 94.2 % | independent | Vals AI |
| MATH Level 5 | high effort | 97.8 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
| OTIS Mock AIME 2024–2025 | high effort | 81.7 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
Agent + model results
systems, not bare-model scores
| agent + model PatchPilot-v1.1 + O4 Mini (2025-04-16) | SWE-bench Verified | 64.6 % | unverified | SWE-bench Leaderboard |
| agent + model GUIRepair + O4 Mini (2025-04-16) | SWE-bench Multimodal | 33.9 % | community | SWE-bench Leaderboard |
| agent + model goose + O4 Mini (2025-04-16) | Terminal-Bench 1.0 | 27.5 % | community | Terminal-Bench Leaderboard |
| agent + model Codex CLI + O4 Mini (2025-04-16) | Terminal-Bench 1.0 | 20.0 % | community | Terminal-Bench Leaderboard |
| agent + model terminus-1 + O4 Mini (2025-04-16) | Terminal-Bench 1.0 | 18.5 % | community | Terminal-Bench Leaderboard |
| agent + model Artificial Analysis harness + O4 Mini (2025-04-16) | Terminal-Bench Hard | 15.2 % | independent | Artificial Analysis |
These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.
