Model comparison
GPT-5.6 Luna vs o3
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 47.4 on the Noometry Index.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. GPT-5.6 Luna scores higher in 9 categories and o3 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.5 to 48.9.
- The biggest single-benchmark swing is ARC-AGI-2: 59.5% for GPT-5.6 Luna and 6.5% for o3.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $2 / $8 for o3.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-5.6 Luna | o3 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 54.7 | 47.4 |
| Released | 2026-07-09 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $0.20 | $2 |
| Output $ / M tokens | $1.20 | $8 |
| Results tracked | 51 | 63 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 53.9 (#25), o3: 47.3 (#57)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| WeirdML | 60.9% | 52.4% |
| LMArena Coding | 1466 | 1408 |
| ALE-Bench | 1,667 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1517 | — |
| SciCode | 53.6% | — |
| GSO | — | 8.8% |
| CadEval | — | 74% |
Agentic & Tool Use GPT-5.6 Luna leads
GPT-5.6 Luna: 35.3 (#37), o3: 34.3 (#42)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.5 (#38), o3: 32.1 (#66)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| ARC-AGI-2 | 59.5% | 6.5% |
| SimpleBench | 46.8% | 53.1% |
| Kagi LLM Benchmark | 49.1% | 67.6% |
| ARC-AGI-1 | 88% | 60.8% |
| CritPt | 20.6% | 1.4% |
| Chess Puzzles | 40% | 38% |
| LMArena Hard Prompts | 1452 | 1402 |
| Mystery Game Puzzles | 21% | 29% |
| DTBench | 89.1% | 84.8% |
| LMCA | 48.5% | 39.7% |
| Epoch Capabilities Index | 156.32 | 146.88 |
| NYT Connections (extended) | 69.4% | — |
| EnigmaEval | — | 13.1% |
| Surface Evolver Bench | 61.9% | — |
| ForecastBench | — | 62.5 |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.5 (#12), o3: 48.9 (#49)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 82.1% | 33.3% |
| OTIS Mock AIME 2024-2025 | 98.3% | 84.4% |
| LMArena Math | 1455 | 1426 |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
| Omni-MATH | — | 71.4% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.6 (#31), o3: 54.5 (#47)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| GPQA Diamond | 91.6% | 81.8% |
| SimpleQA Verified | 41% | 49.4% |
| LMArena Expert | 1479 | 1403 |
| Humanity's Last Exam | — | 20.3% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |
Multimodal GPT-5.6 Luna leads
GPT-5.6 Luna: 43.0 (#22), o3: 40.7 (#35)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| LMArena Vision | 1259 | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual GPT-5.6 Luna leads
GPT-5.6 Luna: 53.0 (#73), o3: 51.9 (#99)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| LMArena Non-English | 1418 | 1402 |
| LMArena Chinese | 1469 | 1438 |
| LMArena French | 1457 | 1430 |
| LMArena German | 1459 | 1420 |
| LMArena Japanese | 1416 | 1404 |
| LMArena Korean | 1414 | 1371 |
| LMArena Russian | 1428 | 1407 |
| LMArena Spanish | 1445 | 1395 |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 76.0 (#48), o3: 73.0 (#122)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| LMArena Instruction Following | 1440 | 1368 |
| IFEval | — | 86.9% |
Long Context o3 leads
GPT-5.6 Luna: 43.9 (#78), o3: 53.2 (#6)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| LMArena Longer Query | 1437 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.3 (#27), o3: 63.7 (#62)
| Benchmark | GPT-5.6 Luna | o3 |
|---|---|---|
| LMArena Text | 1432 | 1410 |
| LMArena Creative Writing | 1397 | 1359 |
| EQ-Bench Creative Writing | 1829 | 1676 |
| LMArena Multi-Turn | 1433 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| WildBench | — | 86.1% |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than o3?
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 47.4 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna or o3?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; o3 lists at $2 and $8.
Is GPT-5.6 Luna or o3 better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 47.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 200K.
How many benchmarks do GPT-5.6 Luna and o3 share?
35 benchmarks have published results for both models. GPT-5.6 Luna has 51 scored results on Noometry and o3 has 63.