# Gemma 3 4B vs GPT-5.6 Luna

> GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 28.1 on the Noometry Index. Gemma 3 4B costs 9.0× 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-3-4b-vs-gpt-5-6-luna
- Last updated: 2026-10-12
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. Gemma 3 4B scores higher in 0 categories and GPT-5.6 Luna in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 16.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.5% for Gemma 3 4B and 98.3% for GPT-5.6 Luna.
- Gemma 3 4B is cheaper at $0.04 / $0.08 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 131K.
- Gemma 3 4B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 28.1 | 54.6 |
| Rank | 326 | 30 |
| Context | 131K | 1.05M |
| Input $/M | $0.04 | $0.20 |
| Output $/M | $0.08 | $1.20 |
| Weights | Open | Proprietary |

## Coding

- Gemma 3 4B: 35.9 (#215)
- GPT-5.6 Luna: 54.5 (#28)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Coding | 1230 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| ALE-Bench | — | 1,667 |

## Agentic & Tool Use

- Gemma 3 4B: 20.9 (#142)
- GPT-5.6 Luna: 34.4 (#45)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| Berkeley Function Calling Leaderboard | 19.6% | — |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |

## Reasoning

- Gemma 3 4B: 13.2 (#335)
- GPT-5.6 Luna: 47.6 (#43)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| Kagi LLM Benchmark | 25.2% | 49.1% |
| Chess Puzzles | 0% | 40% |
| LMArena Hard Prompts | 1253 | 1451 |
| DTBench | 50.9% | 89.1% |
| LMCA | 2.8% | 48.5% |
| Epoch Capabilities Index | 116.02 | 156.39 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Mystery Game Puzzles | — | 21% |
| Surface Evolver Bench | — | 61.9% |

## Math

- Gemma 3 4B: 16.8 (#292)
- GPT-5.6 Luna: 77.7 (#14)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.5% | 98.3% |
| LMArena Math | 1239 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| ProofBench | — | 60% |

## Knowledge

- Gemma 3 4B: 11.8 (#299)
- GPT-5.6 Luna: 58.5 (#34)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 23.2% | 91.6% |
| LMArena Expert | 1223 | 1478 |
| SimpleQA Verified | — | 41% |
| Vectara Hallucination Rate | 6.4% | — |

## Multimodal

- Gemma 3 4B: —
- GPT-5.6 Luna: 42.7 (#28)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |

## Multilingual

- Gemma 3 4B: 42.5 (#194)
- GPT-5.6 Luna: 52.8 (#78)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1273 | 1417 |
| LMArena German | 1281 | 1454 |
| LMArena Russian | 1294 | 1428 |
| LMArena Chinese | — | 1470 |
| LMArena French | — | 1456 |
| LMArena Japanese | — | 1411 |
| LMArena Korean | — | 1415 |
| LMArena Spanish | — | 1448 |

## Instruction Following

- Gemma 3 4B: 65.2 (#225)
- GPT-5.6 Luna: 75.6 (#57)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1239 | 1437 |

## Long Context

- Gemma 3 4B: 38.7 (#194)
- GPT-5.6 Luna: 43.9 (#82)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1273 | 1436 |

## Writing & Preference

- Gemma 3 4B: 42.0 (#239)
- GPT-5.6 Luna: 68.0 (#29)

| Benchmark | Gemma 3 4B | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1291 | 1431 |
| LMArena Creative Writing | 1271 | 1396 |
| EQ-Bench Creative Writing | 1068 | 1829 |
| LMArena Multi-Turn | 1255 | 1434 |
| EQ-Bench 4 | — | 1156 |

## FAQ

### Is Gemma 3 4B better than GPT-5.6 Luna?

GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 28.1 on the Noometry Index. Gemma 3 4B costs 9.0× 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 3 4B or GPT-5.6 Luna?

Gemma 3 4B is cheaper. It lists at $0.04 per million input tokens and $0.08 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.

### Is Gemma 3 4B or GPT-5.6 Luna better for coding?

GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Luna does, with 1.05M tokens against 131K.

### How many benchmarks do Gemma 3 4B and GPT-5.6 Luna share?

20 benchmarks have published results for both models. Gemma 3 4B has 22 scored results on Noometry and GPT-5.6 Luna has 52.