# DeepSeek-R1 vs Gemini 2.0 Flash (Feb 2025)

> DeepSeek-R1 is the stronger model overall, scoring 42.3 to 35.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-gemini-2-0-flash
- Last updated: 2026-10-11
- Shared benchmarks: 42

## Summary

- They share 42 benchmarks with published results for both. DeepSeek-R1 scores higher in 8 categories and Gemini 2.0 Flash (Feb 2025) in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-R1 leads 46.3 to 28.4.
- The biggest single-benchmark swing is Aider Polyglot: 71.4% for DeepSeek-R1 and 38.2% for Gemini 2.0 Flash (Feb 2025).

## Snapshot

| | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 42.3 | 35.1 |
| Rank | 115 | 228 |
| Context | 164K | — |
| Input $/M | $0.50 | — |
| Output $/M | $2.15 | — |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- Gemini 2.0 Flash (Feb 2025): 28.4 (#315)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| Aider Polyglot | 71.4% | 38.2% |
| WeirdML | 41.6% | 25.8% |
| LiveBench Coding | 66.7% | 63.4% |
| LMArena Coding | 1427 | 1350 |
| SWE-bench Verified (bash only) | — | 13.5% |
| SciCode | 35.7% | — |
| BigCodeBench Instruct | — | 45.9% |
| BigCodeBench Complete | — | 59.9% |
| CadEval | — | 30% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- Gemini 2.0 Flash (Feb 2025): 28.1 (#92)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| TheAgentCompany | — | 11.4% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- Gemini 2.0 Flash (Feb 2025): 15.2 (#318)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| ARC-AGI-2 | 1.3% | 1.3% |
| SimpleBench | 40.8% | 31.1% |
| Kagi LLM Benchmark | 69.4% | 37.8% |
| LiveBench Reasoning | 83.2% | 78.2% |
| LMArena Hard Prompts | 1416 | 1346 |
| LiveBench Data Analysis | 69.8% | 69.4% |
| Epoch Capabilities Index | 141.29 | 135.36 |
| LiveBench | 71.6% | 66.9% |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| EnigmaEval | — | 1.1% |
| DTBench | — | 63.2% |
| ForecastBench | 60 | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- Gemini 2.0 Flash (Feb 2025): 37.9 (#146)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 57.8% |
| Omni-MATH | 42.4% | 45.9% |
| LiveBench Math | 80.7% | 75.8% |
| LMArena Math | 1400 | 1352 |
| MATH Level 5 | 96.6% | 82.2% |
| FrontierMath (Feb 2025 set) | — | 1.7% |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- Gemini 2.0 Flash (Feb 2025): 32.0 (#213)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| GPQA Diamond | 76.3% | 64.1% |
| MMLU-Pro | 79.3% | 73.7% |
| Confabulations | 12.7% | 12.4% |
| GPQA (HELM) | 66.6% | 55.6% |
| LMArena Expert | 1394 | 1339 |
| Humanity's Last Exam | — | 6.6% |
| Vectara Hallucination Rate | 11.3% | — |
| MMLU | — | 79.7% |

## Multimodal

- DeepSeek-R1: —
- Gemini 2.0 Flash (Feb 2025): 36.5 (#79)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| LMArena Vision | — | 1158 |
| GeoBench | — | 77% |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- Gemini 2.0 Flash (Feb 2025): 47.4 (#149)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| LMArena Non-English | 1412 | 1342 |
| LMArena Chinese | 1442 | 1373 |
| LMArena French | 1417 | 1391 |
| LMArena German | 1404 | 1353 |
| LMArena Japanese | 1391 | 1294 |
| LMArena Korean | 1360 | 1313 |
| LMArena Russian | 1423 | 1351 |
| LMArena Spanish | 1411 | 1363 |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- Gemini 2.0 Flash (Feb 2025): 74.4 (#97)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| LiveBench Instruction Following | 80.5% | 85.8% |
| IFEval | 78.4% | 84.1% |
| LMArena Instruction Following | 1382 | 1336 |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Gemini 2.0 Flash (Feb 2025): 38.1 (#203)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| Fiction.LiveBench | 75% | 61.1% |
| LMArena Longer Query | 1391 | 1344 |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- Gemini 2.0 Flash (Feb 2025): 49.5 (#190)

| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash (Feb 2025) |
|---|---|---|
| LMArena Text | 1428 | 1354 |
| LMArena Creative Writing | 1405 | 1340 |
| Short-Story Creative Writing | 83% | 73.8% |
| EQ-Bench Creative Writing | 1500 | 1128 |
| WildBench | 82.8% | 80% |
| LMArena Multi-Turn | 1405 | 1350 |
| LiveBench Language | 48.5% | 51.3% |

## FAQ

### Is DeepSeek-R1 better than Gemini 2.0 Flash (Feb 2025)?

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 35.1 on the Noometry Index.

### Is DeepSeek-R1 or Gemini 2.0 Flash (Feb 2025) better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 28.4 in the Noometry coding category.

### How many benchmarks do DeepSeek-R1 and Gemini 2.0 Flash (Feb 2025) share?

42 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Gemini 2.0 Flash (Feb 2025) has 54.