Model comparison
DeepSeek-V3.1 vs Gemma 3 1B
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 21.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
GoogleSummary
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 7.0.
Side by side
| DeepSeek-V3.1 | Gemma 3 1B | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.8 | 21.1 |
| Released | 2025-08-21 | 2025-03-12 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 4 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 40.3 (#144), Gemma 3 1B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Gemma 3 1B: 13.7 (#152)
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 7.2% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Gemma 3 1B: 19.2 (#264)
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1417 | — |
| 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 3 1B: 10.2 (#316)
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 1.1% |
| LMArena Math | 1420 | — |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Gemma 3 1B: 7.0 (#314)
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| GPQA Diamond | — | 19.9% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.6 (#106), Gemma 3 1B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 73.9 (#110), Gemma 3 1B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#232), Gemma 3 1B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.3 (#98), Gemma 3 1B: —
| Benchmark | DeepSeek-V3.1 | Gemma 3 1B |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Gemma 3 1B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 21.1 on the Noometry Index.
How many benchmarks do DeepSeek-V3.1 and Gemma 3 1B share?
0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemma 3 1B has 4.