Model comparison
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.
Last verified . 18 shared benchmarks.
DeepSeek
GoogleSummary
- 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.
Side by side
| DeepSeek-V3.1 | Gemma 3n E4b IT | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.8 | 37.3 |
| Released | 2025-08-21 | — |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 18 |
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Category by category
Coding DeepSeek-V3.1 leads
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 leads
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 leads
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 leads
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 leads
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 leads
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 Gemma 3n E4b IT leads
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 leads
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 | — |
Frequently asked questions
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.