# Claude Opus 5.5 vs DeepSeek-R1

> Claude Opus 5.5 is the stronger model overall, scoring 68.5 to 42.0 on the Noometry Index. DeepSeek-R1 costs 8.8× less per token, which makes it the better buy when Claude Opus 5.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-5-5-vs-deepseek-r1
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Claude Opus 5.5 scores higher in 9 categories and DeepSeek-R1 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 5.5 leads 79.5 to 18.7.
- The biggest single-benchmark swing is ARC-AGI-2: 92.5% for Claude Opus 5.5 and 1.3% for DeepSeek-R1.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $4 / $20 for Claude Opus 5.5.
- Claude Opus 5.5 accepts more context: 1M tokens versus 164K.

## Snapshot

| | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 68.5 | 42.0 |
| Rank | 3 | 110 |
| Context | 1M | 164K |
| Input $/M | $4 | $0.50 |
| Output $/M | $20 | $2.15 |
| Weights | Proprietary | Proprietary |

## Coding

- Claude Opus 5.5: 71.4 (#1)
- DeepSeek-R1: 45.9 (#63)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| SciCode | 66.9% | 35.7% |
| LMArena Coding | 1545 | 1427 |
| FrontierCode | 54.4% | — |
| Aider Polyglot | — | 71.4% |
| CursorBench | 57.8% | — |
| LMArena WebDev | 1820 | — |
| FrontierSWE | 62.3% | — |
| WeirdML | — | 41.6% |
| LiveBench Coding | — | 66.7% |
| MirrorCode | 77.4% | — |
| ALE-Bench | — | 804.12 |
| AlgoTune | — | 1.7 |

## Agentic & Tool Use

- Claude Opus 5.5: 45.3 (#13)
- DeepSeek-R1: 30.4 (#68)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| APEX-Agents | 73.5% | — |
| DeepResearch Bench | — | 35.1% |
| BALROG | — | 34.9% |
| GDP.pdf | 30.6% | — |
| METR Time Horizons | — | 53.8% |

## Reasoning

- Claude Opus 5.5: 79.5 (#2)
- DeepSeek-R1: 18.7 (#253)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| ARC-AGI-2 | 92.5% | 1.3% |
| ARC-AGI-1 | 97.5% | 21.2% |
| CritPt | 31.7% | 1.1% |
| LMArena Hard Prompts | 1545 | 1415 |
| SimpleBench | — | 40.8% |
| Kagi LLM Benchmark | — | 69.4% |
| NYT Connections (extended) | 88.5% | — |
| EBR-Bench | 71.4% | — |
| LiveBench Reasoning | — | 83.2% |
| Mystery Game Puzzles | 71% | — |
| DTBench | 98.9% | — |
| LiveBench Data Analysis | — | 69.8% |
| LMCA | 68.2% | — |
| Epoch Capabilities Index | — | 141.3 |
| ForecastBench | — | 60 |
| LiveBench | — | 71.6% |

## Math

- Claude Opus 5.5: 92.4 (#2)
- DeepSeek-R1: 42.0 (#70)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 66.4% |
| FrontierMath (Tiers 1-3) | 91.2% | — |
| FrontierMath Tier 4 | 95% | — |
| ProofBench | 100% | — |
| Omni-MATH | — | 42.4% |
| LiveBench Math | — | 80.7% |
| LMArena Math | — | 1401 |
| MATH Level 5 | — | 96.6% |

## Knowledge

- Claude Opus 5.5: 66.6 (#9)
- DeepSeek-R1: 44.3 (#81)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| GPQA Diamond | 90.6% | 76.3% |
| LMArena Expert | 1564 | 1394 |
| SimpleQA Verified | 72.2% | — |
| MMLU-Pro | — | 79.3% |
| Confabulations | — | 12.7% |
| Vectara Hallucination Rate | — | 11.3% |
| GPQA (HELM) | — | 66.6% |

## Multimodal

- Claude Opus 5.5: 56.5 (#2)
- DeepSeek-R1: —

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| Furniture Assembly | 83.3% | — |

## Multilingual

- Claude Opus 5.5: 59.8 (#1)
- DeepSeek-R1: 52.6 (#80)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| LMArena Non-English | 1513 | 1412 |
| LMArena Chinese | 1572 | 1443 |
| LMArena Russian | 1532 | 1423 |
| LMArena French | — | 1415 |
| LMArena German | — | 1405 |
| LMArena Japanese | — | 1394 |
| LMArena Korean | — | 1359 |
| LMArena Spanish | — | 1410 |

## Instruction Following

- Claude Opus 5.5: 80.1 (#2)
- DeepSeek-R1: 72.2 (#138)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| LMArena Instruction Following | 1535 | 1382 |
| LiveBench Instruction Following | — | 80.5% |
| IFEval | — | 78.4% |

## Long Context

- Claude Opus 5.5: 47.3 (#18)
- DeepSeek-R1: 45.4 (#36)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| LMArena Longer Query | 1540 | 1391 |
| Fiction.LiveBench | — | 75% |

## Writing & Preference

- Claude Opus 5.5: 78.7 (#3)
- DeepSeek-R1: 61.5 (#84)

| Benchmark | Claude Opus 5.5 | DeepSeek-R1 |
|---|---|---|
| LMArena Text | 1518 | 1428 |
| LMArena Creative Writing | 1538 | 1406 |
| EQ-Bench Creative Writing | 2050 | 1500 |
| LMArena Multi-Turn | 1520 | 1404 |
| Short-Story Creative Writing | — | 83% |
| WildBench | — | 82.8% |
| LiveBench Language | — | 48.5% |

## FAQ

### Is Claude Opus 5.5 better than DeepSeek-R1?

Claude Opus 5.5 is the stronger model overall, scoring 68.5 to 42.0 on the Noometry Index. DeepSeek-R1 costs 8.8× less per token, which makes it the better buy when Claude Opus 5.5's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 5.5 or DeepSeek-R1?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Claude Opus 5.5 lists at $4 and $20.

### Is Claude Opus 5.5 or DeepSeek-R1 better for coding?

Claude Opus 5.5 scores higher on coding benchmarks: 71.4 versus 45.9 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 5.5 does, with 1M tokens against 164K.

### How many benchmarks do Claude Opus 5.5 and DeepSeek-R1 share?

18 benchmarks have published results for both models. Claude Opus 5.5 has 35 scored results on Noometry and DeepSeek-R1 has 52.