OpenAIUnited States· released Jul 2026

GPT-5.6 Luna

Name: GPT-5.6 Luna
Author: OpenAI

Ranked #16, 27.1% behind the frontier on average.

OpenAI, United States. One of 26 models from this lab on the Index.

Third-party publishedMedium confidence (7+ comparable tests, 3+ Index categories)

Available in Theo as Theo Fast

Theo orchestrates it for the steps it does best and always shows which engine answered.

Use it in Theo

Snapshot September 8, 2026

OpenCharts Index

72.9

Ranked #16 (11 to 17) of 36 · 27.1% behind the frontier on average · 18 comparable tests · 3 counting categories

Drop any single test and the Index lands between 70.6 and 75.8, anywhere from #11 to #17. Neighbours inside that range are ties.

Needs more results (3 of 8 so far)

Averaging 70.9 on the 3 it has; the score opens at 4 of 8.

Distance to today's frontier on the hardest tests. Not a measure of intelligence.

What it's known for

GPT-5.6 Luna, in 18 results.

Strongest in math (77.1) and reasoning (73.4). It trails the frontier most in coding, 31.7% behind on average. No comparable result yet in long context. Between 25% and 30% behind the best published results, on average. Drop any single test and it would sit anywhere from #11 to #17.

Strongest category

Math

Competition and research mathematics, graded on the final answer or the proof.

77.1

Mock AIME 2024–202598.3% · 98% of best
FrontierMath (Tiers 1–3)82.1% · 88% of best
FrontierMath Tier 461.0% · 63% of best
ProofBench60.0% · 60% of best

Mean of 4 of the category's 5 comparable tests. Counts toward the Index.

Best single result

Mock AIME 2024–2025

Solve competition mathematics problems that have a single exact integer answer.

98.3%

98.3% of the best (100.0%) · #10 of 78.

Olympiad-style problems need long, careful chains of reasoning, and there is no partial credit.

Where it trails

Code Arena (WebDev)

Build a web app from the same prompt as a rival model; real users vote blind on the result.

1,519

66.3 points short of parity with the board leader (1,797) · #28 of 86.

People judging finished apps side by side is the most honest measure of front-end quality.

Not measured yet

No comparable result yet in this category. A blank is a blank, never a zero, and it does not lower the Index.

Long context

Covered, but not averaged into the Index:

Knowledgesingle-test category, shown beside the Index · 54.2
Agentic1 of 8 tests · needs 4 · mean 67.7
Multimodalsingle-test category, shown beside the Index · 83.0
Human preferencesingle-test category, shown beside the Index · 84.4

Every score, with its source

See it at work

What GPT-5.6 Luna was asked to do, and how it did.

Every test it has taken, by category, with the kind of task it faced, who produced the number and where the result landed against the best published one. The examples are original and illustrative, never items from the datasets themselves.

Human preference · mean 84.4 across 1 comparable test · a single-test category, shown beside the Index and never averaged into it

Text ArenaLeaderboard

Answer the same prompt as a rival model; real users vote blind on which answer they prefer.

1,453

42% expected win rate against the board leader (1,507)
#47 of 92

xhigh · board 2026-09-02

Source: Arena

Every result is read against the best published one on its test: percent scores as a share of the best above chance, Arena ratings as the expected win rate against the board leader, open-ended values on a log scale. Sitting a harder exam never lowers a model. Every number links to the publisher that produced it.

Head to head

GPT-5.6 Luna against whoever you pick.

Choose a rival. Every counted test both have taken appears side by side as a share of the best published result on that test, with the category means above. A win is a higher share; a gap under 2 points is a tie, because one item on a 45-problem exam is 2.2 points and publishers' own standard errors are of that order.

GPT-5.6 Luna

#16·Index 72.9B-

0 : 12

wins · 1 tie · 13 shared

GPT-6 Astra

#1·Index 99.0A+

GPT-6 Astra comes out ahead on 12 of the 13 tests both have taken.

Category means

Reasoning
73.4
99.6
Knowledge
54.2
100.0
Coding
68.3
97.7
Math
77.1
99.8
Agentic
67.7
98.5
Multimodal
83.0
—
Human preference
84.4
—

Shared tests · biggest gaps first

Code Arena (WebDev)Coding
1,519
1,797
−66.3
SimpleQA VerifiedKnowledge
41.0%
75.6%
−45.8
ProofBenchMath
60.0%
99.0%
−39.0
FrontierMath Tier 4Math
61.0%
97.6%
−37.5
ARC-AGI-2Reasoning
59.5%
95.0%
−37.3
WeirdMLCoding
60.9%
92.9%
−34.5
CritPtReasoning
20.6%
31.7%
−34.4
FrontierCodeCoding
39.8%
53.3%
−25.2
FrontierMath (Tiers 1–3)Math
82.1%
93.7%
−12.4
ARC-AGI-1Reasoning
88.0%
98.5%
−10.7
SciCodeCoding
52.5%
56.5%
−6.3
GPQA DiamondReasoning
91.6%
95.8%
−5.9
Mock AIME 2024–2025Math
98.3%
100.0%
tie

Bars are distances to the best published result on each test (percent scores above chance, Arena ratings as win rate against the leader, open-ended values on a log scale). Raw values as the publishers report them.

Open GPT-6 Astra

Scores

Every test, every source

The best published run per benchmark, the raw value as the publisher reports it, who produced the number, how close it comes to the best published result on that test, and where the model sits among every model scored on it. Percent scores are read above chance, Arena ratings as a win rate against the board leader, open-ended values on a log scale. A test with too few models to compare against is shown but not counted.

Category means

Reasoning73.4 · 5/6
Knowledgebeside54.2 · 1/1
Coding68.3 · 5/7
Math77.1 · 4/5
Agentic67.7 · 1/8
Multimodalbeside83.0 · 1/1
Human preferencebeside84.4 · 1/1
Long context—

Mean · tests taken of the category's comparable tests. A faded category either rests on fewer than half of them or is a single-test category (marked “beside”), which is shown next to the Index and never averaged into it.

GPQA DiamondEpoch-runReasoning

91.6%

94.1% of the best (95.8%), above chance
78th percentile of 78 · n = 198

max

GPT-5.6 Luna

GPT-5.6 Luna, in 18 results.

What GPT-5.6 Luna was asked to do, and how it did.

GPT-5.6 Luna against whoever you pick.

Every test, every source

Compare with its neighbours.

The best models, ranked here, working inside Theo.