Model comparison
DeepSeek-V3.1 vs Gemma 3 12B
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 32.1 on the Noometry Index. Gemma 3 12B costs 5.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
DeepSeek
GoogleSummary
- They share 17 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Gemma 3 12B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 26.5.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 48.8% for Gemma 3 12B.
- Gemma 3 12B is cheaper at $0.05 / $0.15 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.1 | Gemma 3 12B | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.8 | 32.1 |
| Released | 2025-08-21 | 2025-03-12 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.25 | $0.05 |
| Output $ / M tokens | $0.95 | $0.15 |
| Results tracked | 27 | 24 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Gemma 3 12B: 31.7 (#280)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Coding | 1417 | 1281 |
| SciCode | — | 17.4% |
| WeirdML | 38.4% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Gemma 3 12B: 25.5 (#108)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 30.4% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Gemma 3 12B: 15.7 (#313)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1309 |
| DTBench | 82.7% | 48.8% |
| LMCA | 24.3% | 4.5% |
| Epoch Capabilities Index | 139.92 | 123.5 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Gemma 3 12B: 22.3 (#279)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Math | 1420 | 1307 |
| OTIS Mock AIME 2024-2025 | — | 16.7% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Gemma 3 12B: 26.5 (#257)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 4.4% |
| LMArena Expert | 1405 | 1248 |
| GPQA Diamond | — | 39.5% |
Multimodal Not comparable
DeepSeek-V3.1: —, Gemma 3 12B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| MindCube | — | 46.7% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Gemma 3 12B: 45.7 (#165)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Non-English | 1400 | 1318 |
| LMArena German | 1411 | 1370 |
| LMArena Russian | 1405 | 1335 |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1431 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Gemma 3 12B: 68.6 (#186)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1299 |
Long Context Gemma 3 12B leads
DeepSeek-V3.1: 36.3 (#232), Gemma 3 12B: 40.0 (#162)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Longer Query | 1422 | 1317 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Gemma 3 12B: 47.5 (#209)
| Benchmark | DeepSeek-V3.1 | Gemma 3 12B |
|---|---|---|
| LMArena Text | 1420 | 1334 |
| LMArena Creative Writing | 1401 | 1331 |
| EQ-Bench Creative Writing | 1436 | 1126 |
| LMArena Multi-Turn | 1408 | 1334 |
Frequently asked questions
Is DeepSeek-V3.1 better than Gemma 3 12B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 32.1 on the Noometry Index. Gemma 3 12B costs 5.7× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Gemma 3 12B?
Gemma 3 12B is cheaper. It lists at $0.05 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Gemma 3 12B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 31.7 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1 does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3.1 and Gemma 3 12B share?
17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemma 3 12B has 24.