Model comparison
DeepSeek-V3.1 vs Gemma 2 2b IT
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.1 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeek
GoogleSummary
- They share 17 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Gemma 2 2b IT in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 36.5.
Side by side
| DeepSeek-V3.1 | Gemma 2 2b IT | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.8 | 33.1 |
| 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 | 17 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Gemma 2 2b IT: 32.3 (#273)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Coding | 1417 | 1112 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Gemma 2 2b IT: 21.4 (#224)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1113 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| 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 2 2b IT: 32.6 (#212)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Math | 1420 | 1135 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Gemma 2 2b IT: 29.9 (#231)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Expert | 1405 | 1096 |
| Vectara Hallucination Rate | 5.5% | — |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Gemma 2 2b IT: 32.3 (#257)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Non-English | 1400 | 1121 |
| LMArena Chinese | 1469 | 1132 |
| LMArena French | 1447 | 1157 |
| LMArena German | 1411 | 1114 |
| LMArena Japanese | 1378 | 1083 |
| LMArena Korean | 1337 | 1055 |
| LMArena Russian | 1405 | 1118 |
| LMArena Spanish | 1431 | 1140 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Gemma 2 2b IT: 57.8 (#263)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Instruction Following | 1400 | 1118 |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#232), Gemma 2 2b IT: 34.3 (#250)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Longer Query | 1422 | 1130 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Gemma 2 2b IT: 36.5 (#263)
| Benchmark | DeepSeek-V3.1 | Gemma 2 2b IT |
|---|---|---|
| LMArena Text | 1420 | 1156 |
| LMArena Creative Writing | 1401 | 1147 |
| LMArena Multi-Turn | 1408 | 1118 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Gemma 2 2b IT?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.1 on the Noometry Index.
Is DeepSeek-V3.1 or Gemma 2 2b IT better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 32.3 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Gemma 2 2b IT share?
17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemma 2 2b IT has 17.