Model comparison
DeepSeek-V3 vs Gemma 2 27B
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 29.4 on the Noometry Index.
Last verified . 34 shared benchmarks.
DeepSeek
GoogleSummary
- They share 34 benchmarks with published results for both. DeepSeek-V3 scores higher in 7 categories and Gemma 2 27B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3 leads 32.1 to 10.7.
- The biggest single-benchmark swing is MATH Level 5: 75.5% for DeepSeek-V3 and 27.9% for Gemma 2 27B.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.65 / $0.65 for Gemma 2 27B.
- DeepSeek-V3 accepts more context: 164K tokens versus 8K.
Side by side
| DeepSeek-V3 | Gemma 2 27B | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.5 | 29.4 |
| Released | 2024-12-26 | 2024-06-24 |
| Weights | Open | Open |
| Context window | 164K | 8K |
| Max output | 164K | 2K |
| Input $ / M tokens | $0.24 | $0.65 |
| Output $ / M tokens | $0.90 | $0.65 |
| Results tracked | 60 | 34 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Gemma 2 27B: 34.1 (#246)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| BigCodeBench Instruct | 50% | 42.8% |
| LiveBench Coding | 70.9% | 36% |
| LMArena Coding | 1368 | 1211 |
| BigCodeBench Complete | 62.2% | 52.5% |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Gemma 2 27B: —
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning DeepSeek-V3 leads
DeepSeek-V3: 20.5 (#236), Gemma 2 27B: 15.3 (#315)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| LiveBench Reasoning | 65.8% | 28.1% |
| LMArena Hard Prompts | 1365 | 1198 |
| DTBench | 64.8% | 48% |
| LiveBench Data Analysis | 60.9% | 47.9% |
| LMCA | 15.5% | 7.1% |
| Epoch Capabilities Index | 135.94 | 122.08 |
| LiveBench | 66.9% | 38.2% |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math DeepSeek-V3 leads
DeepSeek-V3: 32.1 (#219), Gemma 2 27B: 10.7 (#311)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 1.4% |
| LiveBench Math | 73.5% | 26.5% |
| LMArena Math | 1373 | 1212 |
| MATH Level 5 | 75.5% | 27.9% |
| Omni-MATH | 40.3% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), Gemma 2 27B: 19.0 (#280)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| GPQA Diamond | 67.6% | 36.5% |
| Confabulations | 26.1% | 27.1% |
| LMArena Expert | 1351 | 1172 |
| MMLU | 87.2% | 75.7% |
| MMLU-Pro | 72.3% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| TriviaQA | 82.9% | — |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.5 (#143), Gemma 2 27B: 38.6 (#226)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| LMArena Non-English | 1358 | 1217 |
| LMArena Chinese | 1391 | 1221 |
| LMArena French | 1385 | 1247 |
| LMArena German | 1374 | 1209 |
| LMArena Japanese | 1333 | 1175 |
| LMArena Korean | 1319 | 1174 |
| LMArena Russian | 1373 | 1234 |
| LMArena Spanish | 1358 | 1228 |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 72.8 (#130), Gemma 2 27B: 60.5 (#249)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| LiveBench Instruction Following | 81.5% | 58.1% |
| LMArena Instruction Following | 1345 | 1206 |
| IFEval | 83.2% | — |
Long Context Gemma 2 27B leads
DeepSeek-V3: 34.0 (#253), Gemma 2 27B: 37.3 (#218)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| LMArena Longer Query | 1352 | 1231 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Gemma 2 27B: 44.2 (#225)
| Benchmark | DeepSeek-V3 | Gemma 2 27B |
|---|---|---|
| LMArena Text | 1375 | 1231 |
| LMArena Creative Writing | 1364 | 1241 |
| LMArena Multi-Turn | 1389 | 1224 |
| LiveBench Language | 49.1% | 32.6% |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
Frequently asked questions
Is DeepSeek-V3 better than Gemma 2 27B?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 29.4 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or Gemma 2 27B?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Gemma 2 27B lists at $0.65 and $0.65.
Is DeepSeek-V3 or Gemma 2 27B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 8K.
How many benchmarks do DeepSeek-V3 and Gemma 2 27B share?
34 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Gemma 2 27B has 34.