Rankings / Google DeepMind
Gemini 2.5 Pro (2025-06-17)
released 2025-06-17
BenchAtlas Index
as of 2026-07-14
base configuration
rank #189
15 families · 5 categories · high
Benchmark evidence
89 results
agentic coding
| τ²-Bench | 54.1 % | independent | Artificial Analysis | |
| τ²-Bench subset=banking | 9.3 % | independent | Artificial Analysis |
coding
| LiveCodeBench v6 implementation=artificial-analysis | 80.1 % | independent | Artificial Analysis | |
| LiveCodeBench v5older version problems=279 · window_start=2024-08-01 | 69.2 % | independent | LiveCodeBench Leaderboard | |
| SciCode | 42.8 % | independent | Artificial Analysis |
external indices
| AA Coding Index | 33.3 points | independent | Artificial Analysis | |
| Intelligence Index v4.1 | 25.8 points | independent | Artificial Analysis | |
| AA Math Index | 87.7 points | independent | Artificial Analysis | |
| ECI | 146.1 points | independent | Epoch AI Benchmarking Hub | |
| ECI | 8K budget | 146.1 points | independent | Epoch AI Benchmarking Hub |
| ECI | 16K budget | 146.1 points | independent | Epoch AI Benchmarking Hub |
| ECI | 32K budget | 146.1 points | independent | Epoch AI Benchmarking Hub |
factuality
| SimpleQA Verified | 56.0 % | independent | Epoch AI Benchmarking Hub 2025-12-08 |
human preference
| Chinese (style control)older version arena=text · category=chinese · style_control=true | 1489.9 1481.7–1498.2 | 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 | 1470.2 1465.4–1475.0 | community | LMArena Leaderboard Dataset | |
| German (style control)older version arena=text · category=german · style_control=true | 1468.6 1455.3–1482.0 | community | LMArena Leaderboard Dataset | |
| Industry Medicine And Healthcare (style control)older version arena=text · category=industry_medicine_and_healthcare · style_control=true | 1468.0 1460.5–1475.4 | community | LMArena Leaderboard Dataset | |
| Industry Legal And Government (style control)older version arena=text · category=industry_legal_and_government · style_control=true | 1467.9 1461.0–1474.8 | community | LMArena Leaderboard Dataset | |
| Coding (style control)older version arena=text · category=coding · style_control=true | 1465.1 1460.7–1469.4 | community | LMArena Leaderboard Dataset | |
| French (style control)older version arena=text · category=french · style_control=true | 1463.0 1448.5–1477.6 | community | LMArena Leaderboard Dataset | |
| Expert (style control)older version arena=text · category=expert · style_control=true | 1461.1 1453.9–1468.3 | community | LMArena Leaderboard Dataset | |
| Industry Software And It Services (style control)older version arena=text · category=industry_software_and_it_services · style_control=true | 1459.7 1456.1–1463.4 | community | LMArena Leaderboard Dataset | |
| Hard Prompts (style control)older version arena=text · category=hard_prompts · style_control=true | 1458.2 1455.0–1461.4 | community | LMArena Leaderboard Dataset | |
| Polish (style control)older version arena=text · category=polish · style_control=true | 1457.4 1448.6–1466.1 | community | LMArena Leaderboard Dataset | |
| Hard Prompts English (style control)older version arena=text · category=hard_prompts_english · style_control=true | 1456.8 1452.7–1460.8 | community | LMArena Leaderboard Dataset | |
| Longer Query (style control)older version arena=text · category=longer_query · style_control=true | 1456.5 1452.4–1460.6 | community | LMArena Leaderboard Dataset | |
| Russian (style control)older version arena=text · category=russian · style_control=true | 1452.6 1446.3–1458.9 | community | LMArena Leaderboard Dataset | |
| Japanese (style control)older version arena=text · category=japanese · style_control=true | 1451.5 1435.7–1467.4 | community | LMArena Leaderboard Dataset | |
| Spanish (style control)older version arena=text · category=spanish · style_control=true | 1450.1 1437.6–1462.6 | community | LMArena Leaderboard Dataset | |
| Industry Mathematical (style control)older version arena=text · category=industry_mathematical · style_control=true | 1448.4 1440.7–1456.1 | community | LMArena Leaderboard Dataset | |
| Multi Turn (style control)older version arena=text · category=multi_turn · style_control=true | 1448.3 1443.6–1453.1 | community | LMArena Leaderboard Dataset | |
| English (style control)older version arena=text · category=english · style_control=true | 1448.1 1444.8–1451.4 | community | LMArena Leaderboard Dataset | |
| Overall (style control) arena=text · category=overall · style_control=true | 1445.7 1443.2–1448.2 | 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 | 1445.4 1441.1–1449.8 | community | LMArena Leaderboard Dataset | |
| Exclude Ties (style control)older version arena=text · category=exclude_ties · style_control=true | 1445.2 1441.7–1448.7 | community | LMArena Leaderboard Dataset | |
| Creative Writing (style control)older version arena=text · category=creative_writing · style_control=true | 1444.2 1439.0–1449.4 | community | LMArena Leaderboard Dataset | |
| Math (style control)older version arena=text · category=math · style_control=true | 1441.2 1434.2–1448.3 | community | LMArena Leaderboard Dataset | |
| Non English (style control)older version arena=text · category=non_english · style_control=true | 1439.7 1436.5–1442.9 | community | LMArena Leaderboard Dataset | |
| Instruction Following (style control)older version arena=text · category=instruction_following · style_control=true | 1439.5 1435.6–1443.4 | 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 | 1435.9 1431.2–1440.6 | 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 | 1430.4 1425.8–1435.1 | community | LMArena Leaderboard Dataset | |
| Korean (style control)older version arena=text · category=korean · style_control=true | 1415.0 1401.4–1428.6 | community | LMArena Leaderboard Dataset |
knowledge science
| GPQA Diamond | 85.3 % | independent | Epoch AI Benchmarking Hub 2025-11-16 | |
| GPQA Diamond implementation=artificial-analysis | 84.4 % | independent | Artificial Analysis | |
| Humanity's Last Exam implementation=artificial-analysis | 21.1 % | independent | Artificial Analysis | |
| MMLU-Pro implementation=artificial-analysis | 86.2 % | independent | Artificial Analysis |
long context instruction
| AA-LCR | 66.0 % | independent | Artificial Analysis | |
| IFBench | 48.7 % | independent | Artificial Analysis | |
| MultiChallenge | 53.6 % 52.3–54.9 | independent | Scale Labs |
professional
| CaseLaw | 63.9 % | independent | Vals AI | |
| CorpFin | 60.8 % | independent | Vals AI |
reasoning math
| AIME implementation=artificial-analysis | 88.7 % | independent | Artificial Analysis | |
| AIME year=2025 · implementation=artificial-analysis | 87.7 % | independent | Artificial Analysis | |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 16K budget | 56.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 32K budget | 55.9 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 8K budget | 44.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 16K budget | 41.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 32K budget | 37.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 8K budget | 29.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 1K budget | 17.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 1K budget | 16.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 16K budget | 5.1 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 32K budget | 4.9 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 32K budget | 4.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 16K budget | 4.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 8K budget | 2.9 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 8K budget | 2.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 32K budget | 0.8 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 32K budget | 0.8 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 16K budget | 0.7 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 16K budget | 0.7 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 32K budget | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 16K budget | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 8K budget | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 8K budget | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 1K budget | 0.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 32K budget | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 16K budget | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 8K budget | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 8K budget | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 1K budget | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | thinking · 1K budget | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | thinking · 1K budget | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | thinking · 1K budget | 0.0 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | thinking · 1K budget | 0.0 % | independent | ARC Prize Leaderboard |
| FrontierMath | 14.1 % | independent | Epoch AI Benchmarking Hub 2025-11-24 | |
| FrontierMath Tier 4older version | 4.2 % | independent | Epoch AI Benchmarking Hub 2025-07-03 | |
| MATH-500 implementation=artificial-analysis | 96.7 % | independent | Artificial Analysis | |
| OTIS Mock AIME 2024–2025 | 84.2 % | independent | Epoch AI Benchmarking Hub 2025-11-16 |
Agent + model results
systems, not bare-model scores
| agent + model Epoch Inspect harness + Gemini 2.5 Pro (2025-06-17) | SWE-bench Verified | 57.6 % | independent | Epoch AI Benchmarking Hub |
| agent + model mini-SWE-agent + Gemini 2.5 Pro (2025-06-17) | SWE-bench Verified | 53.6 % | community | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Gemini 2.5 Pro (2025-06-17) | SWE-bench bash-only | 53.6 % | community | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Gemini 2.5 Pro (2025-06-17) | SWE-bench bash-only | 0.3 usd_per_task | community | SWE-bench Leaderboard |
| agent + model mini-SWE-agent + Gemini 2.5 Pro (2025-06-17) | SWE-bench Verified | 0.3 usd_per_task | community | SWE-bench Leaderboard |
| agent + model terminus-2 + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 2.0 | 32.6 % | community | Terminal-Bench Leaderboard |
| agent + model Artificial Analysis harness + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 2.1 | 28.5 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench Hard | 26.5 % | independent | Artificial Analysis |
| agent + model mini-SWE-agent + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 2.0 | 26.1 % | community | Terminal-Bench Leaderboard |
| agent + model terminus-1 + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 1.0 | 25.3 % | community | Terminal-Bench Leaderboard |
| agent + model gemini-cli + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 2.0 | 19.6 % | community | Terminal-Bench Leaderboard |
| agent + model OpenHands + Gemini 2.5 Pro (2025-06-17) | Terminal-Bench 2.0 | 16.4 % | community | Terminal-Bench Leaderboard |
These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.
