Rankings / Anthropic
Claude Opus 4.6
released 2026-02-05
BenchAtlas Index
as of 2026-07-14
max effort
rank #19
8 families · 4 categories · medium
max effort
rank #24
8 families · 4 categories · medium
32K budget
rank #39
5 families · 3 categories · medium
Benchmark evidence
124 results
agentic coding
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | high effort | 49.0 % | independent | LiveBench |
| τ²-Bench | max effort | 92.1 % | independent | Artificial Analysis |
| τ²-Bench | high effort | 84.8 % | independent | Artificial Analysis |
coding
| Release 2026-06-25 tasks_counted=2 · livebench_version=2026-06-25 | high effort | 78.2 % | independent | LiveBench |
| SciCode | max effort | 51.9 % | independent | Artificial Analysis |
| SciCode | high effort | 45.7 % | independent | Artificial Analysis |
data analysis
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | high effort | 69.9 % | independent | LiveBench |
external indices
| Intelligence Index v4.1 | max effort | 43.7 points | independent | Artificial Analysis |
| Intelligence Index v4.1 | high effort | 37.8 points | independent | Artificial Analysis |
| ECI | 155.3 points | independent | Epoch AI Benchmarking Hub | |
| ECI | max effort | 155.3 points | independent | Epoch AI Benchmarking Hub |
| ECI | 64K budget | 155.3 points | independent | Epoch AI Benchmarking Hub |
| ECI | medium effort | 155.3 points | independent | Epoch AI Benchmarking Hub |
| ECI | high effort | 155.3 points | independent | Epoch AI Benchmarking Hub |
| ECI | 120K budget | 155.3 points | independent | Epoch AI Benchmarking Hub |
| ECI | 32K budget | 155.3 points | independent | Epoch AI Benchmarking Hub |
factuality
| MASK contamination=Potential contamination warning: This model was evaluated after the public release of MASK, allowing model builder access to the prompts and solutions. | no reasoning | 96.3 % 95.9–96.7 | independent | Scale Labs |
| MASK contamination=Potential contamination warning: This model was evaluated after the public release of MASK, allowing model builder access to the prompts and solutions. | max effort | 85.4 % 84.9–85.9 | independent | Scale Labs |
| SimpleQA Verified | 32K budget | 46.5 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| SimpleQA Verified | 43.1 % | independent | Epoch AI Benchmarking Hub 2026-02-06 | |
| SimpleQA Verified | max effort | 41.0 % | independent | Epoch AI Benchmarking Hub 2026-02-13 |
human preference
| Coding (style control)older version arena=text · category=coding · style_control=true | 1548.0 1542.1–1554.0 | community | LMArena Leaderboard Dataset | |
| Chinese (style control)older version arena=text · category=chinese · style_control=true | 1547.5 1535.7–1559.2 | community | LMArena Leaderboard Dataset | |
| Industry Software And It Services (style control)older version arena=text · category=industry_software_and_it_services · style_control=true | 1536.0 1530.8–1541.3 | community | LMArena Leaderboard Dataset | |
| Expert (style control)older version arena=text · category=expert · style_control=true | 1535.7 1526.7–1544.6 | community | LMArena Leaderboard Dataset | |
| Hard Prompts English (style control)older version arena=text · category=hard_prompts_english · style_control=true | 1531.7 1526.1–1537.4 | community | LMArena Leaderboard Dataset | |
| Hard Prompts (style control)older version arena=text · category=hard_prompts · style_control=true | 1527.2 1522.6–1531.7 | 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 | 1517.2 1510.4–1524.1 | community | LMArena Leaderboard Dataset | |
| Longer Query (style control)older version arena=text · category=longer_query · style_control=true | 1516.9 1511.5–1522.4 | community | LMArena Leaderboard Dataset | |
| Industry Mathematical (style control)older version arena=text · category=industry_mathematical · style_control=true | 1516.7 1505.4–1527.9 | community | LMArena Leaderboard Dataset | |
| Exclude Ties (style control)older version arena=text · category=exclude_ties · style_control=true | 1516.6 1511.6–1521.7 | community | LMArena Leaderboard Dataset | |
| Industry Medicine And Healthcare (style control)older version arena=text · category=industry_medicine_and_healthcare · style_control=true | 1515.6 1505.7–1525.4 | community | LMArena Leaderboard Dataset | |
| Multi Turn (style control)older version arena=text · category=multi_turn · style_control=true | 1511.5 1504.7–1518.3 | community | LMArena Leaderboard Dataset | |
| English (style control)older version arena=text · category=english · style_control=true | 1507.9 1503.1–1512.7 | community | LMArena Leaderboard Dataset | |
| Russian (style control)older version arena=text · category=russian · style_control=true | 1507.8 1499.6–1516.0 | community | LMArena Leaderboard Dataset | |
| Industry Legal And Government (style control)older version arena=text · category=industry_legal_and_government · style_control=true | 1505.7 1496.2–1515.2 | 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 | 1504.0 1497.5–1510.5 | community | LMArena Leaderboard Dataset | |
| Math (style control)older version arena=text · category=math · style_control=true | 1503.5 1492.9–1514.1 | community | LMArena Leaderboard Dataset | |
| Spanish (style control)older version arena=text · category=spanish · style_control=true | 1500.2 1485.0–1515.4 | community | LMArena Leaderboard Dataset | |
| Instruction Following (style control)older version arena=text · category=instruction_following · style_control=true | 1499.5 1494.0–1505.1 | community | LMArena Leaderboard Dataset | |
| Overall (style control) arena=text · category=overall · style_control=true | 1497.8 1494.1–1501.5 | community | LMArena Leaderboard Dataset | |
| Polish (style control)older version arena=text · category=polish · style_control=true | 1497.7 1479.6–1515.8 | community | LMArena Leaderboard Dataset | |
| French (style control)older version arena=text · category=french · style_control=true | 1496.7 1480.8–1512.6 | community | LMArena Leaderboard Dataset | |
| German (style control)older version arena=text · category=german · style_control=true | 1492.3 1471.9–1512.6 | 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 | 1489.8 1483.7–1495.8 | community | LMArena Leaderboard Dataset | |
| Non English (style control)older version arena=text · category=non_english · style_control=true | 1484.3 1479.7–1488.9 | community | LMArena Leaderboard Dataset | |
| Japanese (style control)older version arena=text · category=japanese · style_control=true | 1480.5 1454.0–1507.0 | community | LMArena Leaderboard Dataset | |
| Creative Writing (style control)older version arena=text · category=creative_writing · style_control=true | 1478.4 1471.4–1485.5 | 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 | 1478.2 1471.7–1484.6 | community | LMArena Leaderboard Dataset | |
| Korean (style control)older version arena=text · category=korean · style_control=true | 1458.3 1438.3–1478.2 | community | LMArena Leaderboard Dataset |
knowledge science
| GPQA Diamond | 32K budget | 90.5 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| GPQA Diamond implementation=vals-ai | max effort | 89.6 % | independent | Vals AI |
| GPQA Diamond implementation=artificial-analysis | max effort | 89.6 % | independent | Artificial Analysis |
| GPQA Diamond | 64K budget | 88.8 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| GPQA Diamond implementation=artificial-analysis | high effort | 84.0 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | max effort | 36.7 % | independent | Artificial Analysis |
| Humanity's Last Exam contamination=Potential contamination warning: This model was evaluated after the public release of HLE, allowing model builder access to the prompts and solutions. · implementation=scale | max effort | 34.4 % 32.6–36.3 | independent | Scale Labs |
| Humanity's Last Exam contamination=Potential contamination warning: This model was evaluated after the public release of HLE, allowing model builder access to the prompts and solutions. · implementation=scale | no reasoning | 19.0 % 17.5–20.5 | independent | Scale Labs |
| Humanity's Last Exam implementation=artificial-analysis | high effort | 18.6 % | independent | Artificial Analysis |
| MMLU-Pro implementation=vals-ai | max effort | 89.1 % | independent | Vals AI |
language
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | high effort | 83.3 % | independent | LiveBench |
long context instruction
| AA-LCR | max effort | 70.7 % | independent | Artificial Analysis |
| AA-LCR | high effort | 58.3 % | independent | Artificial Analysis |
| IFBench | max effort | 53.1 % | independent | Artificial Analysis |
| IFBench | high effort | 44.6 % | independent | Artificial Analysis |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | high effort | 63.3 % | independent | LiveBench |
| MultiChallenge | no reasoning | 56.0 % 54.9–57.1 | independent | Scale Labs |
| MultiChallenge | max effort | 37.1 % 35.0–39.3 | independent | Scale Labs |
multimodal
| MMMU implementation=vals-ai | max effort | 83.9 % | independent | Vals AI |
| VISTA | max effort | 46.1 % 45.8–46.3 | independent | Scale Labs |
| VISTA | no reasoning | 45.5 % 45.0–45.9 | independent | Scale Labs |
professional
| CaseLaw | thinking | 62.1 % | independent | Vals AI |
| CorpFin | max effort | 67.0 % | independent | Vals AI |
| LegalBench | max effort | 85.3 % | independent | Vals AI |
| MedQA | thinking | 95.4 % | independent | Vals AI |
| TaxEval | max effort | 76.0 % | independent | Vals AI |
reasoning math
| AIME implementation=vals-ai | thinking | 95.6 % | independent | Vals AI |
| ARC-AGI-1older version split=public_eval · model_type=CoT | max effort · 120K budget | 96.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort · 120K budget | 96.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort · 120K budget | 94.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort · 120K budget | 94.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | max effort · 120K budget | 93.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort · 120K budget | 92.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort · 120K budget | 89.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort · 120K budget | 86.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort · 120K budget | 79.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | max effort · 120K budget | 74.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort · 120K budget | 73.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort · 120K budget | 69.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | max effort · 120K budget | 68.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort · 120K budget | 66.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort · 120K budget | 64.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort · 120K budget | 59.9 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort · 120K budget | 3.8 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | max effort · 120K budget | 3.8 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | max effort · 120K budget | 3.6 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort · 120K budget | 3.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort · 120K budget | 3.0 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort · 120K budget | 2.7 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort · 120K budget | 2.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort · 120K budget | 2.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | max effort · 120K budget | 1.9 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort · 120K budget | 1.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | max effort · 120K budget | 1.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort · 120K budget | 1.0 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort · 120K budget | 0.9 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort · 120K budget | 0.6 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort · 120K budget | 0.6 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-3older version split=semi_private · model_type=CoT | max effort | 0.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort · 120K budget | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| EnigmaEval contamination=Potential contamination warning: This model was evaluated after the public release of EnigmaEval, allowing model builder access to the prompts and solutions. | max effort | 7.6 % 6.1–9.1 | independent | Scale Labs |
| EnigmaEval contamination=Potential contamination warning: This model was evaluated after the public release of EnigmaEval, allowing model builder access to the prompts and solutions. | no reasoning | 6.8 % 5.4–8.3 | independent | Scale Labs |
| FrontierMath | max effort | 40.7 % | independent | Epoch AI Benchmarking Hub 2026-02-12 |
| FrontierMath | 32K budget | 40.0 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| FrontierMath | 64K budget | 39.7 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| FrontierMath | 38.3 % | independent | Epoch AI Benchmarking Hub 2026-02-05 | |
| FrontierMath Tier 4older version | max effort | 22.9 % | independent | Epoch AI Benchmarking Hub 2026-02-12 |
| FrontierMath Tier 4older version | 64K budget | 20.8 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| FrontierMath Tier 4older version | 32K budget | 20.8 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| FrontierMath Tier 4older version | 14.6 % | independent | Epoch AI Benchmarking Hub 2026-02-05 | |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | high effort | 89.3 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | high effort | 88.7 % | independent | LiveBench |
| OTIS Mock AIME 2024–2025 | 64K budget | 94.4 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
| OTIS Mock AIME 2024–2025 | 32K budget | 93.1 % | independent | Epoch AI Benchmarking Hub 2026-02-06 |
Agent + model results
systems, not bare-model scores
| agent + model Epoch Inspect harness + Claude Opus 4.6 | SWE-bench Verified | 78.7 % | independent | Epoch AI Benchmarking Hub |
| agent + model Epoch Inspect harness + Claude Opus 4.6 | SWE-bench Verified | 75.6 % | independent | Epoch AI Benchmarking Hub |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench bash-only | 75.6 % | unverified | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench Verified | 75.6 % | unverified | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench Multilingual | 72.0 % | community | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench Multilingual | 0.7 usd_per_task | community | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench Verified | 0.6 usd_per_task | unverified | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Claude Opus 4.6 | SWE-bench bash-only | 0.6 usd_per_task | unverified | SWE-bench Leaderboard |
| agent + model terminus-kira-env-bootstrap + Claude Opus 4.6 | Terminal-Bench 2.0 | 76.4 % | unverified | Terminal-Bench Leaderboard |
| agent + model capy-build + Claude Opus 4.6 | Terminal-Bench 2.0 | 75.3 % | unverified | Terminal-Bench Leaderboard |
| agent + model terminus-3-3 + Claude Opus 4.6 | Terminal-Bench 2.0 | 74.7 % | unverified | Terminal-Bench Leaderboard |
| agent + model judy + Claude Opus 4.6 | Terminal-Bench 2.0 | 71.9 % | unverified | Terminal-Bench Leaderboard |
| agent + model Droid + Claude Opus 4.6 | Terminal-Bench 2.0 | 69.9 % | unverified | Terminal-Bench Leaderboard |
| agent + model Crux + Claude Opus 4.6 | Terminal-Bench 2.0 | 66.9 % | unverified | Terminal-Bench Leaderboard |
| agent + model mux + Claude Opus 4.6 | Terminal-Bench 2.0 | 66.5 % | unverified | Terminal-Bench Leaderboard |
| agent + model terminus-2 + Claude Opus 4.6 | Terminal-Bench 2.0 | 62.9 % | community | Terminal-Bench Leaderboard |
| agent + model Claude Code + Claude Opus 4.6 | Terminal-Bench 2.0 | 58.0 % | community | Terminal-Bench Leaderboard |
| agent + model Artificial Analysis harness + Claude Opus 4.6 | Terminal-Bench Hard | 48.5 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + Claude Opus 4.6 | Terminal-Bench Hard | 46.2 % | independent | Artificial Analysis |
These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.
