Rankings / OpenAI
GPT 5.6 Luna
released 2026-07-09
BenchAtlas Index
as of 2026-09-11
max effort
rank #35
7 families · 4 categories · medium
high effort
rank #56
7 families · 5 categories · medium
medium effort
rank #88
7 families · 5 categories · medium
Benchmark evidence
123 results
agentic coding
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | xhigh effort | 48.8 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | max effort | 48.4 % | independent | LiveBench |
| τ²-Bench subset=banking | max effort | 31.1 % | independent | Artificial Analysis |
| τ²-Bench subset=banking | xhigh effort | 28.7 % | independent | Artificial Analysis |
| τ²-Bench subset=banking | high effort | 25.1 % | independent | Artificial Analysis |
| τ²-Bench subset=banking | medium effort | 17.7 % | independent | Artificial Analysis |
| τ²-Bench subset=banking | low effort | 12.8 % | independent | Artificial Analysis |
| τ²-Bench subset=banking | no reasoning | 9.7 % | independent | Artificial Analysis |
coding
| Release 2026-06-25 tasks_counted=2 · livebench_version=2026-06-25 | max effort | 82.9 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=2 · livebench_version=2026-06-25 | xhigh effort | 76.7 % | independent | LiveBench |
| SciCode | max effort | 53.6 % | independent | Artificial Analysis |
| SciCode | high effort | 51.6 % | independent | Artificial Analysis |
| SciCode | xhigh effort | 50.5 % | independent | Artificial Analysis |
| SciCode | medium effort | 46.8 % | independent | Artificial Analysis |
| SciCode | low effort | 46.1 % | independent | Artificial Analysis |
| SciCode | no reasoning | 39.9 % | independent | Artificial Analysis |
data analysis
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | max effort | 78.0 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | xhigh effort | 72.9 % | independent | LiveBench |
external indices
factuality
| SimpleQA Verified | max effort | 41.7 % | independent | Epoch AI Benchmarking Hub 2026-07-09 |
| SimpleQA Verified | max effort | 41.0 % | independent | Epoch AI Benchmarking Hub 2026-08-10 |
knowledge science
| GPQA Diamond implementation=vals-ai | max effort | 91.7 % | independent | Vals AI |
| GPQA Diamond | max effort | 91.6 % | independent | Epoch AI Benchmarking Hub 2026-07-09 |
| GPQA Diamond implementation=artificial-analysis | max effort | 91.1 % | independent | Artificial Analysis |
| GPQA Diamond implementation=artificial-analysis | xhigh effort | 89.5 % | independent | Artificial Analysis |
| GPQA Diamond implementation=artificial-analysis | high effort | 89.2 % | independent | Artificial Analysis |
| GPQA Diamond implementation=artificial-analysis | medium effort | 85.9 % | independent | Artificial Analysis |
| GPQA Diamond implementation=artificial-analysis | low effort | 83.5 % | independent | Artificial Analysis |
| GPQA Diamond | low effort | 82.3 % | independent | Epoch AI Benchmarking Hub 2026-08-07 |
| GPQA Diamond implementation=artificial-analysis | no reasoning | 64.5 % | independent | Artificial Analysis |
| GPQA Diamond | no reasoning | 63.6 % | independent | Epoch AI Benchmarking Hub 2026-08-07 |
| Humanity's Last Exam implementation=artificial-analysis | max effort | 39.5 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | xhigh effort | 37.0 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | high effort | 33.4 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | medium effort | 25.8 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | low effort | 19.8 % | independent | Artificial Analysis |
| Humanity's Last Exam implementation=artificial-analysis | no reasoning | 7.2 % | independent | Artificial Analysis |
| MMLU-Pro implementation=vals-ai | max effort | 86.0 % | independent | Vals AI |
language
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | max effort | 72.6 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=3 · livebench_version=2026-06-25 | xhigh effort | 70.2 % | independent | LiveBench |
long context instruction
| AA-LCR | max effort | 83.7 % | independent | Artificial Analysis |
| AA-LCR | xhigh effort | 81.7 % | independent | Artificial Analysis |
| AA-LCR | high effort | 80.3 % | independent | Artificial Analysis |
| AA-LCR | medium effort | 75.0 % | independent | Artificial Analysis |
| AA-LCR | low effort | 70.0 % | independent | Artificial Analysis |
| AA-LCR | no reasoning | 42.7 % | independent | Artificial Analysis |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | max effort | 60.1 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | xhigh effort | 57.5 % | independent | LiveBench |
multimodal
| MMMU implementation=vals-ai | max effort | 85.0 % | independent | Vals AI |
professional
| CorpFin | max effort | 64.2 % | independent | Vals AI |
| LegalBench | max effort | 84.0 % | independent | Vals AI |
| TaxEval | max effort | 76.2 % | independent | Vals AI |
reasoning math
| ARC-AGI-1older version split=public_eval · model_type=CoT | max effort | 90.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | xhigh effort | 90.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | max effort | 88.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | xhigh effort | 87.7 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort | 79.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 76.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort | 64.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | max effort | 60.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | max effort | 59.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort | 56.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | xhigh effort | 51.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | xhigh effort | 47.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort | 47.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort | 34.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 30.1 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 29.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 7.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 7.4 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort | 5.1 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort | 2.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | max effort | 0.7 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | max effort | 0.7 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | xhigh effort | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | xhigh effort | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | max effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | xhigh effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | max effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-3older version split=semi_private · model_type=CoT | max effort | 0.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-3older version split=semi_private · model_type=CoT | low effort | 0.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-3older version split=semi_private · model_type=CoT | medium effort | 0.2 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | xhigh effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 0.1 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-3older version split=semi_private · model_type=CoT | high effort | 0.1 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high 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 | 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-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 |
| ARC-AGI-3older version split=semi_private · model_type=CoT | xhigh effort | 0.0 % | independent | ARC Prize Leaderboard |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | max effort | 87.2 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | xhigh effort | 86.3 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | max effort | 85.6 % | independent | LiveBench |
| Release 2026-06-25 tasks_counted=4 · livebench_version=2026-06-25 | xhigh effort | 84.7 % | independent | LiveBench |
| OTIS Mock AIME 2024–2025 | max effort | 98.3 % | independent | Epoch AI Benchmarking Hub 2026-07-09 |
| OTIS Mock AIME 2024–2025 | low effort | 66.7 % | independent | Epoch AI Benchmarking Hub 2026-08-07 |
| OTIS Mock AIME 2024–2025 | no reasoning | 40.0 % | independent | Epoch AI Benchmarking Hub 2026-08-07 |
Agent + model results
systems, not bare-model scores
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 80.9 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 77.9 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 69.7 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 53.2 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 43.5 % | independent | Artificial Analysis |
| agent + model Artificial Analysis harness + GPT 5.6 Luna | Terminal-Bench 2.1 | 39.0 % | independent | Artificial Analysis |
These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.
