Model comparison
Gemini 3 Pro vs GPT-5.6 Luna
Gemini 3 Pro and GPT-5.6 Luna score almost the same on the Noometry Index (54.8 vs 54.6), so choose on price, context window or the category you care about most.
Last verified . 36 shared benchmarks.
GoogleSummary
- They share 36 benchmarks with published results for both. Gemini 3 Pro scores higher in 7 categories and GPT-5.6 Luna in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 49.9.
- The biggest single-benchmark swing is ProofBench: 20% for Gemini 3 Pro and 60% for GPT-5.6 Luna.
Side by side
| Gemini 3 Pro | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.8 | 54.6 |
| Released | 2025-11-18 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $0.20 |
| Output $ / M tokens | — | $1.20 |
| Results tracked | 67 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Gemini 3 Pro: 51.6 (#39), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena WebDev | 1440 | 1519 |
| WeirdML | 69.9% | 60.9% |
| LMArena Coding | 1481 | 1466 |
| ALE-Bench | 1,177 | 1,667 |
| SWE-bench Verified | 72.9% | — |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| SWE-bench Verified (bash only) | 74.2% | — |
| CursorBench | — | 35.9% |
| SWE-bench Multilingual | 68.7% | — |
| SciCode | — | 53.6% |
| GSO | 18.6% | — |
| AlgoTune | 1.83 | — |
Agentic & Tool Use Gemini 3 Pro leads
Gemini 3 Pro: 40.6 (#23), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| BALROG | 58.1% | 45.6% |
| Vending-Bench 2 | 5,478 | 4,095 |
| Terminal-Bench | 69.4% | — |
| APEX-Agents | — | 43% |
| Berkeley Function Calling Leaderboard | 72.5% | — |
| GDPval | 40.3% | — |
| Remote Labor Index | 1.3% | — |
| τ²-bench Airline | 80.5% | — |
| τ²-bench Banking | 18% | — |
| τ²-bench Retail | 75.9% | — |
| τ²-bench Telecom | 91% | — |
| DeepResearch Bench | 46.3% | — |
| GDP.pdf | — | 22.7% |
| LMArena Search | 1207 | — |
| METR Time Horizons | 71% | — |
Reasoning Gemini 3 Pro leads
Gemini 3 Pro: 52.5 (#31), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| ARC-AGI-2 | 31.1% | 59.5% |
| SimpleBench | 76.4% | 46.8% |
| Kagi LLM Benchmark | 80.1% | 49.1% |
| NYT Connections (extended) | 94.4% | 69.4% |
| ARC-AGI-1 | 75% | 88% |
| CritPt | 6.9% | 20.6% |
| Chess Puzzles | 31% | 40% |
| LMArena Hard Prompts | 1480 | 1451 |
| Epoch Capabilities Index | 152.92 | 156.39 |
| EnigmaEval | 18.2% | — |
| Mystery Game Puzzles | — | 21% |
| DTBench | — | 89.1% |
| LMCA | — | 48.5% |
| Surface Evolver Bench | — | 61.9% |
| ForecastBench | 61.2 | — |
Math GPT-5.6 Luna leads
Gemini 3 Pro: 49.9 (#59), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.4% | 98.3% |
| ProofBench | 20% | 60% |
| LMArena Math | 1476 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| MathArena Final-Answer Competitions | 67% | — |
| Omni-MATH | 55.5% | — |
| FrontierMath (Feb 2025 set) | 37.6% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge Gemini 3 Pro leads
Gemini 3 Pro: 64.4 (#16), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 92.6% | 91.6% |
| LMArena Expert | 1475 | 1478 |
| Humanity's Last Exam | 37.5% | — |
| SimpleQA Verified | — | 41% |
| MMLU-Pro | 90.3% | — |
| Vectara Hallucination Rate | 13.6% | — |
| GPQA (HELM) | 80.3% | — |
Multimodal Gemini 3 Pro leads
Gemini 3 Pro: 57.6 (#2), GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | 1305 | 1258 |
| LMArena Document | 1434 | 1457 |
| GeoBench | 84% | — |
| VPCT | 91% | — |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
Multilingual Gemini 3 Pro leads
Gemini 3 Pro: 56.9 (#16), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1474 | 1417 |
| LMArena Chinese | 1523 | 1470 |
| LMArena French | 1492 | 1456 |
| LMArena German | 1515 | 1454 |
| LMArena Japanese | 1510 | 1411 |
| LMArena Korean | 1448 | 1415 |
| LMArena Russian | 1493 | 1428 |
| LMArena Spanish | 1470 | 1448 |
Instruction Following Too close to call
Gemini 3 Pro: 76.3 (#45), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1458 | 1437 |
| IFEval | 87.7% | — |
Long Context Too close to call
Gemini 3 Pro: 44.0 (#79), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1471 | 1436 |
| CL-bench | 15.8% | — |
Writing & Preference GPT-5.6 Luna leads
Gemini 3 Pro: 66.4 (#35), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Gemini 3 Pro | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1479 | 1431 |
| LMArena Creative Writing | 1482 | 1396 |
| EQ-Bench Creative Writing | 1525 | 1829 |
| LMArena Multi-Turn | 1484 | 1434 |
| WildBench | 85.9% | — |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is Gemini 3 Pro better than GPT-5.6 Luna?
Gemini 3 Pro and GPT-5.6 Luna score almost the same on the Noometry Index (54.8 vs 54.6), so choose on price, context window or the category you care about most.
Is Gemini 3 Pro or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 51.6 in the Noometry coding category.
How many benchmarks do Gemini 3 Pro and GPT-5.6 Luna share?
36 benchmarks have published results for both models. Gemini 3 Pro has 67 scored results on Noometry and GPT-5.6 Luna has 52.