Model comparison
DeepSeek-V3.1 vs Gemma 1.1 7b IT
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.7 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 1.1 7b IT in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 30.6.
Side by side
| DeepSeek-V3.1 | Gemma 1.1 7b IT | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.2 | 30.7 |
| 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 | 19 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Gemma 1.1 7b IT: 31.0 (#279)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Coding | 1417 | 1084 |
| WeirdML | 38.4% | — |
| HumanEval+ | — | 35.4% |
| MBPP+ | — | 45% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Gemma 1.1 7b IT: 19.6 (#235)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1071 |
| 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: 36.1 (#125), Gemma 1.1 7b IT: 29.5 (#217)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Math | 1420 | 1107 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Gemma 1.1 7b IT: 27.8 (#242)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Expert | 1403 | 1039 |
| Vectara Hallucination Rate | 5.5% | — |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.8 (#100), Gemma 1.1 7b IT: 28.3 (#268)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Non-English | 1400 | 1052 |
| LMArena Chinese | 1470 | 1060 |
| LMArena French | 1447 | 1064 |
| LMArena German | 1412 | 1054 |
| LMArena Japanese | 1374 | 971 |
| LMArena Korean | 1337 | 988 |
| LMArena Russian | 1405 | 1046 |
| LMArena Spanish | 1429 | 1049 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), Gemma 1.1 7b IT: 54.3 (#278)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Instruction Following | 1400 | 1057 |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#227), Gemma 1.1 7b IT: 32.0 (#267)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Longer Query | 1422 | 1056 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), Gemma 1.1 7b IT: 30.6 (#283)
| Benchmark | DeepSeek-V3.1 | Gemma 1.1 7b IT |
|---|---|---|
| LMArena Text | 1420 | 1094 |
| LMArena Creative Writing | 1401 | 1059 |
| LMArena Multi-Turn | 1408 | 1040 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Gemma 1.1 7b IT?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.7 on the Noometry Index.
Is DeepSeek-V3.1 or Gemma 1.1 7b IT better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 31.0 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Gemma 1.1 7b IT share?
17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemma 1.1 7b IT has 19.