Model comparison
DeepSeek-V3.2-Speciale vs Gemma 7B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.0 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
GoogleSummary
- The widest gap is in writing & preference, where DeepSeek-V3.2-Speciale leads 46.0 to 27.1.
Side by side
| DeepSeek-V3.2-Speciale | Gemma 7B | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.7 | 30.0 |
| Released | 2025-12-01 | 2024-02-21 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 128K | — |
| Input $ / M tokens | $0.58 | — |
| Output $ / M tokens | $1.68 | — |
| Results tracked | 3 | 27 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Gemma 7B: 30.5 (#294)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1048 |
| HumanEval+ | — | 28.7% |
| MBPP+ | — | 43.4% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Gemma 7B: 19.9 (#249)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1042 |
| Adversarial NLI | — | 48.7% |
| BIG-Bench Hard | — | 55.1% |
| Epoch Capabilities Index | — | 111.99 |
| HellaSwag | — | 82.2% |
| PIQA | — | 81.2% |
| WinoGrande | — | 79% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Gemma 7B: 31.2 (#228)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Math | — | 1066 |
| GSM8K | — | 46.4% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Gemma 7B: 27.3 (#252)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Expert | — | 1001 |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 83.2% |
| MMLU | — | 66.1% |
| OpenBookQA | — | 78.6% |
| TriviaQA | — | 72.3% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Gemma 7B: 25.1 (#287)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Non-English | — | 999 |
| LMArena Chinese | — | 1035 |
| LMArena French | — | 1025 |
| LMArena Russian | — | 993 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Gemma 7B: 51.5 (#295)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Instruction Following | — | 1017 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Gemma 7B: 31.1 (#282)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Longer Query | — | 1022 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Gemma 7B: 27.1 (#302)
| Benchmark | DeepSeek-V3.2-Speciale | Gemma 7B |
|---|---|---|
| LMArena Text | — | 1056 |
| LMArena Creative Writing | — | 1024 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 963 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Gemma 7B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.0 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Gemma 7B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 30.5 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Gemma 7B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Gemma 7B has 27.