Model comparison
Gemini 1.5 Flash (May 2024) vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 33.2 on the Noometry Index.
Last verified . 24 shared benchmarks.
GoogleSummary
- They share 24 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 0 categories and GPT-5.6 Luna in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 22.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 16.3% for Gemini 1.5 Flash (May 2024) and 98.3% for GPT-5.6 Luna.
Side by side
| Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 33.2 | 54.6 |
| Released | 2024-05-14 | 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 | 42 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 34.4 (#236), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| WeirdML | 24.9% | 60.9% |
| LMArena Coding | 1261 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
| BigCodeBench Instruct | 43.5% | — |
| BigCodeBench Complete | 55.1% | — |
| ALE-Bench | — | 1,667 |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 26.6 (#102), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| BALROG | 14.6% | 45.6% |
| APEX-Agents | — | 43% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 21.7 (#215), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1451 |
| DTBench | 53.8% | 89.1% |
| Epoch Capabilities Index | 129.36 | 156.39 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 21% |
| LMCA | — | 48.5% |
| Surface Evolver Bench | — | 61.9% |
| ForecastBench | 53.9 | — |
| PIQA | 87.5% | — |
Math GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 22.1 (#281), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | 98.3% |
| LMArena Math | 1269 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| ProofBench | — | 60% |
| Omni-MATH | 30.4% | — |
| MATH Level 5 | 61.9% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
| GSM8K | 82.4% | — |
Knowledge GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 26.2 (#260), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 47.3% | 91.6% |
| LMArena Expert | 1233 | 1478 |
| SimpleQA Verified | — | 41% |
| MMLU-Pro | 67.8% | — |
| GPQA (HELM) | 43.7% | — |
| BoolQ | 85.8% | — |
| MMLU | 77.9% | — |
Multimodal GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 36.0 (#81), GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | 1141 | 1258 |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 42.9 (#189), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1278 | 1417 |
| LMArena Chinese | 1295 | 1470 |
| LMArena French | 1258 | 1456 |
| LMArena German | 1262 | 1454 |
| LMArena Japanese | 1252 | 1411 |
| LMArena Korean | 1221 | 1415 |
| LMArena Russian | 1288 | 1428 |
| LMArena Spanish | 1243 | 1448 |
Instruction Following GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 66.8 (#205), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1258 | 1437 |
| IFEval | 83.1% | — |
Long Context GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 39.0 (#187), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1284 | 1436 |
Writing & Preference GPT-5.6 Luna leads
Gemini 1.5 Flash (May 2024): 48.7 (#196), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1287 | 1431 |
| LMArena Creative Writing | 1285 | 1396 |
| LMArena Multi-Turn | 1253 | 1434 |
| EQ-Bench Creative Writing | — | 1829 |
| WildBench | 79.2% | — |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 33.2 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 34.4 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Flash (May 2024) and GPT-5.6 Luna share?
24 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and GPT-5.6 Luna has 52.