# DeepSeek-V3.1 vs Gemma 3n E4b IT

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-gemma-3n-e4b-it
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Gemma 3n E4b IT in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 50.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 31.5% for Gemma 3n E4b IT.

## Snapshot

| | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 42.8 | 37.3 |
| Rank | 108 | 206 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Gemma 3n E4b IT: 37.0 (#198)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Coding | 1417 | 1268 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Gemma 3n E4b IT: 19.9 (#247)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 31.5% |
| LMArena Hard Prompts | 1417 | 1284 |
| SimpleBench | 40% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Gemma 3n E4b IT: 35.1 (#188)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Math | 1420 | 1251 |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Gemma 3n E4b IT: 34.2 (#198)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Expert | 1405 | 1246 |
| Vectara Hallucination Rate | 5.5% | — |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Gemma 3n E4b IT: 43.4 (#183)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Non-English | 1400 | 1285 |
| LMArena Chinese | 1469 | 1309 |
| LMArena French | 1447 | 1330 |
| LMArena German | 1411 | 1311 |
| LMArena Japanese | 1378 | 1272 |
| LMArena Korean | 1337 | 1259 |
| LMArena Russian | 1405 | 1288 |
| LMArena Spanish | 1431 | 1305 |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Gemma 3n E4b IT: 66.1 (#210)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Instruction Following | 1400 | 1255 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Gemma 3n E4b IT: 38.7 (#191)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Longer Query | 1422 | 1276 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Gemma 3n E4b IT: 50.1 (#186)

| Benchmark | DeepSeek-V3.1 | Gemma 3n E4b IT |
|---|---|---|
| LMArena Text | 1420 | 1306 |
| LMArena Creative Writing | 1401 | 1287 |
| LMArena Multi-Turn | 1408 | 1276 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Gemma 3n E4b IT?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.3 on the Noometry Index.

### Is DeepSeek-V3.1 or Gemma 3n E4b IT better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 37.0 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Gemma 3n E4b IT share?

18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemma 3n E4b IT has 18.