Model comparison
DeepSeek-R1-Distill-Qwen-32B vs Gemma 3n E4b IT
Gemma 3n E4b IT is the stronger model overall, scoring 37.3 to 35.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
GoogleSummary
- The widest gap is in instruction following, where Gemma 3n E4b IT leads 66.1 to 61.6.
Side by side
| DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 35.5 | 37.3 |
| Released | 2025-01-20 | — |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 14 | 18 |
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Category by category
Coding Too close to call
DeepSeek-R1-Distill-Qwen-32B: 36.1 (#212), Gemma 3n E4b IT: 37.0 (#198)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| BigCodeBench Instruct | 43.9% | — |
| LiveBench Coding | 33.7% | — |
| LMArena Coding | — | 1268 |
| BigCodeBench Complete | 54.9% | — |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-32B: 28.1 (#94), Gemma 3n E4b IT: —
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| BALROG | 19.5% | — |
Reasoning Gemma 3n E4b IT leads
DeepSeek-R1-Distill-Qwen-32B: 18.2 (#284), Gemma 3n E4b IT: 19.9 (#247)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| Kagi LLM Benchmark | — | 31.5% |
| Chess Puzzles | 1% | — |
| LiveBench Reasoning | 52.3% | — |
| LMArena Hard Prompts | — | 1284 |
| LiveBench Data Analysis | 45.4% | — |
| Epoch Capabilities Index | 137.44 | — |
| LiveBench | 45.5% | — |
Math Too close to call
DeepSeek-R1-Distill-Qwen-32B: 34.5 (#194), Gemma 3n E4b IT: 35.1 (#188)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 55.6% | — |
| LiveBench Math | 59.4% | — |
| LMArena Math | — | 1251 |
Knowledge DeepSeek-R1-Distill-Qwen-32B leads
DeepSeek-R1-Distill-Qwen-32B: 35.7 (#182), Gemma 3n E4b IT: 34.2 (#198)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| GPQA Diamond | 64.1% | — |
| LMArena Expert | — | 1246 |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-32B: —, Gemma 3n E4b IT: 43.4 (#183)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| LMArena Non-English | — | 1285 |
| LMArena Chinese | — | 1309 |
| LMArena French | — | 1330 |
| LMArena German | — | 1311 |
| LMArena Japanese | — | 1272 |
| LMArena Korean | — | 1259 |
| LMArena Russian | — | 1288 |
| LMArena Spanish | — | 1305 |
Instruction Following Gemma 3n E4b IT leads
DeepSeek-R1-Distill-Qwen-32B: 61.6 (#243), Gemma 3n E4b IT: 66.1 (#210)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| LiveBench Instruction Following | 55.7% | — |
| LMArena Instruction Following | — | 1255 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-32B: —, Gemma 3n E4b IT: 38.7 (#191)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| LMArena Longer Query | — | 1276 |
Writing & Preference Too close to call
DeepSeek-R1-Distill-Qwen-32B: 49.6 (#188), Gemma 3n E4b IT: 50.1 (#186)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Gemma 3n E4b IT |
|---|---|---|
| LMArena Text | — | 1306 |
| LMArena Creative Writing | — | 1287 |
| LMArena Multi-Turn | — | 1276 |
| LiveBench Language | 26.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-32B better than Gemma 3n E4b IT?
Gemma 3n E4b IT is the stronger model overall, scoring 37.3 to 35.5 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-32B or Gemma 3n E4b IT better for coding?
They score almost the same on coding (36.1 vs 37.0); test both on your own repository before choosing.
How many benchmarks do DeepSeek-R1-Distill-Qwen-32B and Gemma 3n E4b IT share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-32B has 14 scored results on Noometry and Gemma 3n E4b IT has 18.