Model comparison
DeepSeek-V3 vs Gemma 3 27B
DeepSeek-V3 is the stronger model overall, scoring 39.2 to 30.5 on the Noometry Index. Gemma 3 27B costs 4.0× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.
Last verified . 39 shared benchmarks.
DeepSeek
GoogleSummary
- They share 39 benchmarks with published results for both. DeepSeek-V3 scores higher in 8 categories and Gemma 3 27B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3 leads 41.9 to 22.1.
- The biggest single-benchmark swing is Aider Polyglot: 55.1% for DeepSeek-V3 and 4.9% for Gemma 3 27B.
- Gemma 3 27B is cheaper at $0.08 / $0.16 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3 | Gemma 3 27B | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.2 | 30.5 |
| Released | 2024-12-26 | 2025-03-11 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 164K | 8K |
| Input $ / M tokens | $0.24 | $0.08 |
| Output $ / M tokens | $0.90 | $0.16 |
| Results tracked | 60 | 43 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 41.9 (#99), Gemma 3 27B: 22.1 (#329)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| Aider Polyglot | 55.1% | 4.9% |
| SciCode | 35.8% | 21.2% |
| LiveBench Coding | 70.9% | 39.9% |
| LMArena Coding | 1368 | 1322 |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Gemma 3 27B: 24.9 (#105)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 29.5% |
| METR Time Horizons | 49.6% | — |
Reasoning DeepSeek-V3 leads
DeepSeek-V3: 20.0 (#226), Gemma 3 27B: 16.3 (#297)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 40.4% |
| CritPt | 0% | 0% |
| LiveBench Reasoning | 65.8% | 43.8% |
| LMArena Hard Prompts | 1365 | 1340 |
| DTBench | 64.8% | 52.5% |
| LiveBench Data Analysis | 60.9% | 51.5% |
| LMCA | 15.5% | 12.3% |
| Epoch Capabilities Index | 135.95 | 130.04 |
| LiveBench | 66.9% | 50% |
| SimpleBench | 27.2% | — |
| Chess Puzzles | — | 0% |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math DeepSeek-V3 leads
DeepSeek-V3: 30.4 (#208), Gemma 3 27B: 24.4 (#261)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 22.5% |
| LiveBench Math | 73.5% | 55.4% |
| LMArena Math | 1373 | 1312 |
| MATH Level 5 | 75.5% | 74% |
| Omni-MATH | 40.3% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#147), Gemma 3 27B: 25.4 (#256)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| GPQA Diamond | 67.6% | 47.7% |
| Confabulations | 26.1% | 40.3% |
| Vectara Hallucination Rate | 6.1% | 7.4% |
| LMArena Expert | 1352 | 1303 |
| MMLU-Pro | 72.3% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, Gemma 3 27B: 32.0 (#94)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| LMArena Vision | — | 1165 |
| GeoBench | — | 52% |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.8 (#138), Gemma 3 27B: 47.0 (#150)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| LMArena Non-English | 1358 | 1334 |
| LMArena Chinese | 1390 | 1347 |
| LMArena French | 1386 | 1368 |
| LMArena German | 1374 | 1362 |
| LMArena Japanese | 1333 | 1287 |
| LMArena Korean | 1321 | 1308 |
| LMArena Russian | 1372 | 1349 |
| LMArena Spanish | 1358 | 1348 |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 73.0 (#125), Gemma 3 27B: 70.8 (#155)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| LiveBench Instruction Following | 81.5% | 74.9% |
| LMArena Instruction Following | 1345 | 1321 |
| IFEval | 83.2% | — |
Long Context DeepSeek-V3 leads
DeepSeek-V3: 34.0 (#248), Gemma 3 27B: 27.6 (#288)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| Fiction.LiveBench | 50% | 33.3% |
| LMArena Longer Query | 1353 | 1333 |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.6 (#124), Gemma 3 27B: 52.7 (#161)
| Benchmark | DeepSeek-V3 | Gemma 3 27B |
|---|---|---|
| LMArena Text | 1375 | 1358 |
| LMArena Creative Writing | 1364 | 1346 |
| Short-Story Creative Writing | 77% | 79.9% |
| EQ-Bench Creative Writing | 1472 | 1266 |
| LMArena Multi-Turn | 1389 | 1344 |
| LiveBench Language | 49.1% | 34.6% |
| WildBench | 83% | — |
Frequently asked questions
Is DeepSeek-V3 better than Gemma 3 27B?
DeepSeek-V3 is the stronger model overall, scoring 39.2 to 30.5 on the Noometry Index. Gemma 3 27B costs 4.0× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or Gemma 3 27B?
Gemma 3 27B is cheaper. It lists at $0.08 per million input tokens and $0.16 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Gemma 3 27B better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 22.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3 and Gemma 3 27B share?
39 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Gemma 3 27B has 43.