# DeepSeek-R1 vs Gemma 2 27B

> DeepSeek-R1 is the stronger model overall, scoring 42.0 to 29.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-gemma-2-27b
- Last updated: 2026-10-10
- Shared benchmarks: 29

## Summary

- They share 29 benchmarks with published results for both. DeepSeek-R1 scores higher in 8 categories and Gemma 2 27B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 42.0 to 10.0.
- The biggest single-benchmark swing is MATH Level 5: 96.6% for DeepSeek-R1 and 27.9% for Gemma 2 27B.
- Gemma 2 27B is cheaper at $0.65 / $0.65 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 8K.
- Gemma 2 27B has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 42.0 | 29.2 |
| Rank | 110 | 302 |
| Context | 164K | 8K |
| Input $/M | $0.50 | $0.65 |
| Output $/M | $2.15 | $0.65 |
| Weights | Proprietary | Open |

## Coding

- DeepSeek-R1: 45.9 (#63)
- Gemma 2 27B: 33.5 (#243)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LiveBench Coding | 66.7% | 36% |
| LMArena Coding | 1427 | 1211 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| BigCodeBench Instruct | — | 42.8% |
| BigCodeBench Complete | — | 52.5% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Gemma 2 27B: —

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Gemma 2 27B: 14.8 (#313)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LiveBench Reasoning | 83.2% | 28.1% |
| LMArena Hard Prompts | 1415 | 1199 |
| LiveBench Data Analysis | 69.8% | 47.9% |
| Epoch Capabilities Index | 141.3 | 122.06 |
| LiveBench | 71.6% | 38.2% |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| DTBench | — | 48% |
| LMCA | — | 7.1% |
| ForecastBench | 60 | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Gemma 2 27B: 10.0 (#306)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 1.4% |
| LiveBench Math | 80.7% | 26.5% |
| LMArena Math | 1401 | 1212 |
| MATH Level 5 | 96.6% | 27.9% |
| Omni-MATH | 42.4% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Gemma 2 27B: 18.9 (#275)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| GPQA Diamond | 76.3% | 36.5% |
| Confabulations | 12.7% | 27.1% |
| LMArena Expert | 1394 | 1172 |
| MMLU-Pro | 79.3% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| MMLU | — | 75.7% |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Gemma 2 27B: 38.8 (#221)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LMArena Non-English | 1412 | 1217 |
| LMArena Chinese | 1443 | 1221 |
| LMArena French | 1415 | 1246 |
| LMArena German | 1405 | 1209 |
| LMArena Japanese | 1394 | 1175 |
| LMArena Korean | 1359 | 1174 |
| LMArena Russian | 1423 | 1234 |
| LMArena Spanish | 1410 | 1228 |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Gemma 2 27B: 60.7 (#244)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LiveBench Instruction Following | 80.5% | 58.1% |
| LMArena Instruction Following | 1382 | 1206 |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Gemma 2 27B: 37.3 (#213)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LMArena Longer Query | 1391 | 1231 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Gemma 2 27B: 44.4 (#220)

| Benchmark | DeepSeek-R1 | Gemma 2 27B |
|---|---|---|
| LMArena Text | 1428 | 1231 |
| LMArena Creative Writing | 1406 | 1241 |
| LMArena Multi-Turn | 1404 | 1224 |
| LiveBench Language | 48.5% | 32.6% |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |

## FAQ

### Is DeepSeek-R1 better than Gemma 2 27B?

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 29.2 on the Noometry Index.

### Which is cheaper, DeepSeek-R1 or Gemma 2 27B?

Gemma 2 27B is cheaper. It lists at $0.65 per million input tokens and $0.65 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

### Is DeepSeek-R1 or Gemma 2 27B better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 33.5 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-R1 does, with 164K tokens against 8K.

### How many benchmarks do DeepSeek-R1 and Gemma 2 27B share?

29 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Gemma 2 27B has 34.