Model comparison
Gemma 2 27B vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 29.4 on the Noometry Index.
Last verified . 22 shared benchmarks.
GoogleSummary
- They share 22 benchmarks with published results for both. Gemma 2 27B scores higher in 0 categories and GPT-5.6 Luna in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 10.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.4% for Gemma 2 27B and 98.3% for GPT-5.6 Luna.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $0.65 / $0.65 for Gemma 2 27B.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 8K.
- Gemma 2 27B has downloadable open weights; the other is API-only.
Side by side
| Gemma 2 27B | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 29.4 | 54.6 |
| Released | 2024-06-24 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 8K | 1.05M |
| Max output | 2K | 128K |
| Input $ / M tokens | $0.65 | $0.20 |
| Output $ / M tokens | $0.65 | $1.20 |
| Results tracked | 34 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Gemma 2 27B: 34.1 (#246), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Coding | 1211 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | — | 1,667 |
Agentic & Tool Use Not comparable
Gemma 2 27B: —, GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
Gemma 2 27B: 15.3 (#315), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Hard Prompts | 1198 | 1451 |
| DTBench | 48% | 89.1% |
| LMCA | 7.1% | 48.5% |
| Epoch Capabilities Index | 122.08 | 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% |
| LiveBench Reasoning | 28.1% | — |
| Mystery Game Puzzles | — | 21% |
| LiveBench Data Analysis | 47.9% | — |
| Surface Evolver Bench | — | 61.9% |
| LiveBench | 38.2% | — |
Math GPT-5.6 Luna leads
Gemma 2 27B: 10.7 (#311), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.4% | 98.3% |
| LMArena Math | 1212 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| ProofBench | — | 60% |
| LiveBench Math | 26.5% | — |
| MATH Level 5 | 27.9% | — |
Knowledge GPT-5.6 Luna leads
Gemma 2 27B: 19.0 (#280), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 36.5% | 91.6% |
| LMArena Expert | 1172 | 1478 |
| SimpleQA Verified | — | 41% |
| Confabulations | 27.1% | — |
| MMLU | 75.7% | — |
Multimodal Not comparable
Gemma 2 27B: —, GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual GPT-5.6 Luna leads
Gemma 2 27B: 38.6 (#226), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1217 | 1417 |
| LMArena Chinese | 1221 | 1470 |
| LMArena French | 1247 | 1456 |
| LMArena German | 1209 | 1454 |
| LMArena Japanese | 1175 | 1411 |
| LMArena Korean | 1174 | 1415 |
| LMArena Russian | 1234 | 1428 |
| LMArena Spanish | 1228 | 1448 |
Instruction Following GPT-5.6 Luna leads
Gemma 2 27B: 60.5 (#249), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1206 | 1437 |
| LiveBench Instruction Following | 58.1% | — |
Long Context GPT-5.6 Luna leads
Gemma 2 27B: 37.3 (#218), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1231 | 1436 |
Writing & Preference GPT-5.6 Luna leads
Gemma 2 27B: 44.2 (#225), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Gemma 2 27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1231 | 1431 |
| LMArena Creative Writing | 1241 | 1396 |
| LMArena Multi-Turn | 1224 | 1434 |
| EQ-Bench Creative Writing | — | 1829 |
| EQ-Bench 4 | — | 1156 |
| LiveBench Language | 32.6% | — |
Frequently asked questions
Is Gemma 2 27B better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 29.4 on the Noometry Index.
Which is cheaper, Gemma 2 27B or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Gemma 2 27B lists at $0.65 and $0.65.
Is Gemma 2 27B or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 8K.
How many benchmarks do Gemma 2 27B and GPT-5.6 Luna share?
22 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and GPT-5.6 Luna has 52.