BenchAtlas

Rankings / OpenAI

GPT 5.6 Luna

released 2026-07-09

BenchAtlas Index

as of 2026-09-11
75.5
max effort
rank #35
7 families · 4 categories · medium
72.6
high effort
rank #56
7 families · 5 categories · medium
67.6
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 effort48.8 %independentLiveBench
Release 2026-06-25
tasks_counted=3 · livebench_version=2026-06-25
max effort48.4 %independentLiveBench
τ²-Bench
subset=banking
max effort31.1 %independentArtificial Analysis
τ²-Bench
subset=banking
xhigh effort28.7 %independentArtificial Analysis
τ²-Bench
subset=banking
high effort25.1 %independentArtificial Analysis
τ²-Bench
subset=banking
medium effort17.7 %independentArtificial Analysis
τ²-Bench
subset=banking
low effort12.8 %independentArtificial Analysis
τ²-Bench
subset=banking
no reasoning9.7 %independentArtificial Analysis
coding
Release 2026-06-25
tasks_counted=2 · livebench_version=2026-06-25
max effort82.9 %independentLiveBench
Release 2026-06-25
tasks_counted=2 · livebench_version=2026-06-25
xhigh effort76.7 %independentLiveBench
SciCodemax effort53.6 %independentArtificial Analysis
SciCodehigh effort51.6 %independentArtificial Analysis
SciCodexhigh effort50.5 %independentArtificial Analysis
SciCodemedium effort46.8 %independentArtificial Analysis
SciCodelow effort46.1 %independentArtificial Analysis
SciCodeno reasoning39.9 %independentArtificial Analysis
data analysis
Release 2026-06-25
tasks_counted=3 · livebench_version=2026-06-25
max effort78.0 %independentLiveBench
Release 2026-06-25
tasks_counted=3 · livebench_version=2026-06-25
xhigh effort72.9 %independentLiveBench
external indices
AA Coding Indexmax effort71.4 pointsindependentArtificial Analysis
AA Coding Indexxhigh effort68.6 pointsindependentArtificial Analysis
AA Coding Indexhigh effort63.3 pointsindependentArtificial Analysis
AA Coding Indexmedium effort50.7 pointsindependentArtificial Analysis
AA Coding Indexlow effort44.2 pointsindependentArtificial Analysis
AA Coding Indexno reasoning39.3 pointsindependentArtificial Analysis
Intelligence Index v4.1max effort37.5 pointsindependentArtificial Analysis
Intelligence Index v4.1xhigh effort34.8 pointsindependentArtificial Analysis
Intelligence Index v4.1high effort32.4 pointsindependentArtificial Analysis
Intelligence Index v4.1medium effort25.8 pointsindependentArtificial Analysis
Intelligence Index v4.1low effort21.8 pointsindependentArtificial Analysis
Intelligence Index v4.1no reasoning16.8 pointsindependentArtificial Analysis
ECIxhigh effort156.3 pointsindependentEpoch AI Benchmarking Hub
ECImax effort156.3 pointsindependentEpoch AI Benchmarking Hub
ECIno reasoning156.3 pointsindependentEpoch AI Benchmarking Hub
ECIlow effort156.3 pointsindependentEpoch AI Benchmarking Hub
ECImedium effort156.3 pointsindependentEpoch AI Benchmarking Hub
ECIhigh effort156.3 pointsindependentEpoch AI Benchmarking Hub
ECI156.3 pointsindependentEpoch AI Benchmarking Hub
Vals Indexmax effort59.9 pointsindependentVals AI
factuality
SimpleQA Verifiedmax effort41.7 %independentEpoch AI Benchmarking Hub
2026-07-09
SimpleQA Verifiedmax effort41.0 %independentEpoch AI Benchmarking Hub
2026-08-10
knowledge science
GPQA Diamond
implementation=vals-ai
max effort91.7 %independentVals AI
GPQA Diamondmax effort91.6 %independentEpoch AI Benchmarking Hub
2026-07-09
GPQA Diamond
implementation=artificial-analysis
max effort91.1 %independentArtificial Analysis
GPQA Diamond
implementation=artificial-analysis
xhigh effort89.5 %independentArtificial Analysis
GPQA Diamond
implementation=artificial-analysis
high effort89.2 %independentArtificial Analysis
GPQA Diamond
implementation=artificial-analysis
medium effort85.9 %independentArtificial Analysis
GPQA Diamond
implementation=artificial-analysis
low effort83.5 %independentArtificial Analysis
GPQA Diamondlow effort82.3 %independentEpoch AI Benchmarking Hub
2026-08-07
GPQA Diamond
implementation=artificial-analysis
no reasoning64.5 %independentArtificial Analysis
GPQA Diamondno reasoning63.6 %independentEpoch AI Benchmarking Hub
2026-08-07
Humanity's Last Exam
implementation=artificial-analysis
max effort39.5 %independentArtificial Analysis
Humanity's Last Exam
implementation=artificial-analysis
xhigh effort37.0 %independentArtificial Analysis
Humanity's Last Exam
implementation=artificial-analysis
high effort33.4 %independentArtificial Analysis
Humanity's Last Exam
implementation=artificial-analysis
medium effort25.8 %independentArtificial Analysis
Humanity's Last Exam
implementation=artificial-analysis
low effort19.8 %independentArtificial Analysis
Humanity's Last Exam
implementation=artificial-analysis
no reasoning7.2 %independentArtificial Analysis
MMLU-Pro
implementation=vals-ai
max effort86.0 %independentVals AI
language
Release 2026-06-25
tasks_counted=3 · livebench_version=2026-06-25
max effort72.6 %independentLiveBench
Release 2026-06-25
tasks_counted=3 · livebench_version=2026-06-25
xhigh effort70.2 %independentLiveBench
long context instruction
AA-LCRmax effort83.7 %independentArtificial Analysis
AA-LCRxhigh effort81.7 %independentArtificial Analysis
AA-LCRhigh effort80.3 %independentArtificial Analysis
AA-LCRmedium effort75.0 %independentArtificial Analysis
AA-LCRlow effort70.0 %independentArtificial Analysis
AA-LCRno reasoning42.7 %independentArtificial Analysis
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
max effort60.1 %independentLiveBench
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
xhigh effort57.5 %independentLiveBench
multimodal
MMMU
implementation=vals-ai
max effort85.0 %independentVals AI
professional
CorpFinmax effort64.2 %independentVals AI
LegalBenchmax effort84.0 %independentVals AI
TaxEvalmax effort76.2 %independentVals AI
reasoning math
ARC-AGI-1older version
split=public_eval · model_type=CoT
max effort90.8 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
xhigh effort90.0 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
max effort88.0 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
xhigh effort87.7 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
high effort79.3 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
high effort76.5 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
medium effort64.4 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
max effort60.2 %independentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
max effort59.5 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
medium effort56.5 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
xhigh effort51.4 %independentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
xhigh effort47.6 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
low effort47.4 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
low effort34.2 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
high effort30.1 %independentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
high effort29.3 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
medium effort7.6 %independentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
medium effort7.4 %independentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
low effort5.1 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
low effort2.8 %independentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
max effort0.7 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
max effort0.7 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
xhigh effort0.5 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
xhigh effort0.5 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
max effort0.3 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
high effort0.3 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
high effort0.3 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
xhigh effort0.2 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
max effort0.2 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-3older version
split=semi_private · model_type=CoT
max effort0.2 %independentARC Prize Leaderboard
ARC-AGI-3older version
split=semi_private · model_type=CoT
low effort0.2 %independentARC Prize Leaderboard
ARC-AGI-3older version
split=semi_private · model_type=CoT
medium effort0.2 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
xhigh effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
high effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
medium effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
medium effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-3older version
split=semi_private · model_type=CoT
high effort0.1 %independentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
high effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=public_eval · model_type=CoT
low effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-2
split=semi_private · model_type=CoT
low effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
medium effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
medium effort0.1 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=semi_private · model_type=CoT
low effort0.0 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-1older version
split=public_eval · model_type=CoT
low effort0.0 usd_per_taskindependentARC Prize Leaderboard
ARC-AGI-3older version
split=semi_private · model_type=CoT
xhigh effort0.0 %independentARC Prize Leaderboard
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
max effort87.2 %independentLiveBench
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
xhigh effort86.3 %independentLiveBench
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
max effort85.6 %independentLiveBench
Release 2026-06-25
tasks_counted=4 · livebench_version=2026-06-25
xhigh effort84.7 %independentLiveBench
OTIS Mock AIME 2024–2025max effort98.3 %independentEpoch AI Benchmarking Hub
2026-07-09
OTIS Mock AIME 2024–2025low effort66.7 %independentEpoch AI Benchmarking Hub
2026-08-07
OTIS Mock AIME 2024–2025no reasoning40.0 %independentEpoch AI Benchmarking Hub
2026-08-07

Agent + model results

systems, not bare-model scores
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.180.9 %independentArtificial Analysis
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.177.9 %independentArtificial Analysis
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.169.7 %independentArtificial Analysis
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.153.2 %independentArtificial Analysis
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.143.5 %independentArtificial Analysis
agent + model Artificial Analysis harness + GPT 5.6 LunaTerminal-Bench 2.139.0 %independentArtificial Analysis

These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.