# Claude 2.1 vs DeepSeek-R1

> DeepSeek-R1 is the stronger model overall, scoring 42.3 to 25.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/claude-2-1-vs-deepseek-r1
- Last updated: 2026-10-11
- Shared benchmarks: 5

## Summary

- They share 5 benchmarks with published results for both. Claude 2.1 scores higher in 1 category and DeepSeek-R1 in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.9% for Claude 2.1 and 66.4% for DeepSeek-R1.

## Snapshot

| | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 25.2 | 42.3 |
| Rank | 345 | 115 |
| Context | — | 164K |
| Input $/M | — | $0.50 |
| Output $/M | — | $2.15 |
| Weights | Proprietary | Proprietary |

## Coding

- Claude 2.1: 26.2 (#327)
- DeepSeek-R1: 46.3 (#68)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| WeirdML | 7.1% | 41.6% |
| Aider Polyglot | — | 71.4% |
| SciCode | — | 35.7% |
| LiveBench Coding | — | 66.7% |
| LMArena Coding | — | 1427 |
| ALE-Bench | — | 804.12 |
| AlgoTune | — | 1.7 |

## Agentic & Tool Use

- Claude 2.1: —
- DeepSeek-R1: 30.7 (#75)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| DeepResearch Bench | — | 35.1% |
| BALROG | — | 34.9% |
| METR Time Horizons | — | 53.8% |

## Reasoning

- Claude 2.1: 21.4 (#221)
- DeepSeek-R1: 18.6 (#278)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| Epoch Capabilities Index | 119.27 | 141.29 |
| ForecastBench | 54.2 | 60 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 40.8% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 21.2% |
| CritPt | — | 1.1% |
| LiveBench Reasoning | — | 83.2% |
| LMArena Hard Prompts | — | 1416 |
| DTBench | 51% | — |
| LiveBench Data Analysis | — | 69.8% |
| LiveBench | — | 71.6% |

## Math

- Claude 2.1: 10.2 (#315)
- DeepSeek-R1: 43.8 (#79)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 66.4% |
| Omni-MATH | — | 42.4% |
| LiveBench Math | — | 80.7% |
| LMArena Math | — | 1400 |
| MATH Level 5 | — | 96.6% |

## Knowledge

- Claude 2.1: 15.4 (#292)
- DeepSeek-R1: 44.5 (#87)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| GPQA Diamond | 33% | 76.3% |
| MMLU-Pro | — | 79.3% |
| Confabulations | — | 12.7% |
| Vectara Hallucination Rate | — | 11.3% |
| GPQA (HELM) | — | 66.6% |
| LMArena Expert | — | 1394 |
| MMLU | 73.5% | — |

## Multilingual

- Claude 2.1: —
- DeepSeek-R1: 52.4 (#85)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| LMArena Non-English | — | 1412 |
| LMArena Chinese | — | 1442 |
| LMArena French | — | 1417 |
| LMArena German | — | 1404 |
| LMArena Japanese | — | 1391 |
| LMArena Korean | — | 1360 |
| LMArena Russian | — | 1423 |
| LMArena Spanish | — | 1411 |

## Instruction Following

- Claude 2.1: —
- DeepSeek-R1: 72.0 (#143)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| LiveBench Instruction Following | — | 80.5% |
| IFEval | — | 78.4% |
| LMArena Instruction Following | — | 1382 |

## Long Context

- Claude 2.1: —
- DeepSeek-R1: 45.4 (#36)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| Fiction.LiveBench | — | 75% |
| LMArena Longer Query | — | 1391 |

## Writing & Preference

- Claude 2.1: —
- DeepSeek-R1: 61.4 (#88)

| Benchmark | Claude 2.1 | DeepSeek-R1 |
|---|---|---|
| LMArena Text | — | 1428 |
| LMArena Creative Writing | — | 1405 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1500 |
| WildBench | — | 82.8% |
| LMArena Multi-Turn | — | 1405 |
| LiveBench Language | — | 48.5% |

## FAQ

### Is Claude 2.1 better than DeepSeek-R1?

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 25.2 on the Noometry Index.

### Is Claude 2.1 or DeepSeek-R1 better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 26.2 in the Noometry coding category.

### How many benchmarks do Claude 2.1 and DeepSeek-R1 share?

5 benchmarks have published results for both models. Claude 2.1 has 7 scored results on Noometry and DeepSeek-R1 has 52.