# Gemma 3n E4b IT vs GPT-5.6 Luna

> GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 36.7 on the Noometry Index. Gemma 3n E4b IT costs 6.0× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemma-3n-e4b-it-vs-gpt-5-6-luna
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Gemma 3n E4b IT 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 77.5 to 32.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 31.5% for Gemma 3n E4b IT and 49.1% for GPT-5.6 Luna.
- Gemma 3n E4b IT is cheaper at $0.06 / $0.12 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 33K.
- Gemma 3n E4b IT has downloadable open weights; the other is API-only.

## Snapshot

| | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 36.7 | 54.7 |
| Rank | 203 | 24 |
| Context | 33K | 1.05M |
| Input $/M | $0.06 | $0.20 |
| Output $/M | $0.12 | $1.20 |
| Weights | Open | Proprietary |

## Coding

- Gemma 3n E4b IT: 36.4 (#193)
- GPT-5.6 Luna: 53.9 (#25)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Coding | 1269 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1517 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| ALE-Bench | — | 1,667 |

## Agentic & Tool Use

- Gemma 3n E4b IT: —
- GPT-5.6 Luna: 35.3 (#37)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |

## Reasoning

- Gemma 3n E4b IT: 19.2 (#239)
- GPT-5.6 Luna: 47.5 (#38)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| Kagi LLM Benchmark | 31.5% | 49.1% |
| LMArena Hard Prompts | 1284 | 1452 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 21% |
| DTBench | — | 89.1% |
| LMCA | — | 48.5% |
| Surface Evolver Bench | — | 61.9% |
| Epoch Capabilities Index | — | 156.32 |

## Math

- Gemma 3n E4b IT: 32.5 (#186)
- GPT-5.6 Luna: 77.5 (#12)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Math | 1251 | 1455 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| OTIS Mock AIME 2024-2025 | — | 98.3% |
| ProofBench | — | 60% |

## Knowledge

- Gemma 3n E4b IT: 33.6 (#193)
- GPT-5.6 Luna: 58.6 (#31)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Expert | 1245 | 1479 |
| GPQA Diamond | — | 91.6% |
| SimpleQA Verified | — | 41% |

## Multimodal

- Gemma 3n E4b IT: —
- GPT-5.6 Luna: 43.0 (#22)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1259 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |

## Multilingual

- Gemma 3n E4b IT: 43.5 (#179)
- GPT-5.6 Luna: 53.0 (#73)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1285 | 1418 |
| LMArena Chinese | 1310 | 1469 |
| LMArena French | 1330 | 1457 |
| LMArena German | 1312 | 1459 |
| LMArena Japanese | 1274 | 1416 |
| LMArena Korean | 1259 | 1414 |
| LMArena Russian | 1288 | 1428 |
| LMArena Spanish | 1305 | 1445 |

## Instruction Following

- Gemma 3n E4b IT: 66.3 (#205)
- GPT-5.6 Luna: 76.0 (#48)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1255 | 1440 |

## Long Context

- Gemma 3n E4b IT: 38.8 (#185)
- GPT-5.6 Luna: 43.9 (#78)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1277 | 1437 |

## Writing & Preference

- Gemma 3n E4b IT: 50.3 (#181)
- GPT-5.6 Luna: 68.3 (#27)

| Benchmark | Gemma 3n E4b IT | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1306 | 1432 |
| LMArena Creative Writing | 1287 | 1397 |
| LMArena Multi-Turn | 1276 | 1433 |
| EQ-Bench Creative Writing | — | 1829 |
| EQ-Bench 4 | — | 1156 |

## FAQ

### Is Gemma 3n E4b IT better than GPT-5.6 Luna?

GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 36.7 on the Noometry Index. Gemma 3n E4b IT costs 6.0× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.

### Which is cheaper, Gemma 3n E4b IT or GPT-5.6 Luna?

Gemma 3n E4b IT is cheaper. It lists at $0.06 per million input tokens and $0.12 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.

### Is Gemma 3n E4b IT or GPT-5.6 Luna better for coding?

GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 36.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Luna does, with 1.05M tokens against 33K.

### How many benchmarks do Gemma 3n E4b IT and GPT-5.6 Luna share?

18 benchmarks have published results for both models. Gemma 3n E4b IT has 18 scored results on Noometry and GPT-5.6 Luna has 51.