# Claude Opus 5.5 vs DeepSeek-V3.1

> Claude Opus 5.5 is the stronger model overall, scoring 68.6 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 19× 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-v3-1
- Last updated: 2026-10-12
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Claude Opus 5.5 scores higher in 8 categories and DeepSeek-V3.1 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 5.5 leads 91.8 to 38.9.
- The biggest single-benchmark swing is LMCA: 68.2% for Claude Opus 5.5 and 24.3% for DeepSeek-V3.1.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $4 / $20 for Claude Opus 5.5.
- Claude Opus 5.5 accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 68.6 | 42.8 |
| Rank | 3 | 108 |
| Context | 1M | 164K |
| Input $/M | $4 | $0.25 |
| Output $/M | $20 | $0.95 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 5.5: 71.9 (#3)
- DeepSeek-V3.1: 40.3 (#144)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Coding | 1547 | 1417 |
| FrontierCode | 54.6% | — |
| CursorBench | 57.8% | — |
| LMArena WebDev | 1813 | — |
| FrontierSWE | 62.3% | — |
| SciCode | 66.9% | — |
| WeirdML | — | 38.4% |
| MirrorCode | 77.4% | — |
| ALE-Bench | 2,147 | — |

## Agentic & Tool Use

- Claude Opus 5.5: 45.3 (#15)
- DeepSeek-V3.1: —

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| APEX-Agents | 73.5% | — |
| GDP.pdf | 30.6% | — |
| Vending-Bench 2 | 9,235 | — |

## Reasoning

- Claude Opus 5.5: 80.2 (#3)
- DeepSeek-V3.1: 27.9 (#110)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Hard Prompts | 1535 | 1417 |
| DTBench | 98.9% | 82.7% |
| LMCA | 68.2% | 24.3% |
| Epoch Capabilities Index | 167.33 | 139.92 |
| ARC-AGI-2 | 93.3% | — |
| SimpleBench | — | 40% |
| Kagi LLM Benchmark | — | 53.2% |
| NYT Connections (extended) | 88.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| EBR-Bench | 71.4% | — |
| Mystery Game Puzzles | 71% | — |
| ForecastBench | — | 58 |

## Math

- Claude Opus 5.5: 91.8 (#3)
- DeepSeek-V3.1: 38.9 (#122)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Math | 1506 | 1420 |
| FrontierMath (Tiers 1-3) | 91.2% | — |
| FrontierMath Tier 4 | 95% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 100% | — |
| FrontierMath Erdős | 2.9% | — |

## Knowledge

- Claude Opus 5.5: 66.4 (#10)
- DeepSeek-V3.1: 43.7 (#90)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Expert | 1547 | 1405 |
| GPQA Diamond | 90.6% | — |
| SimpleQA Verified | 72.2% | — |
| Vectara Hallucination Rate | — | 5.5% |

## Multimodal

- Claude Opus 5.5: 57.8 (#1)
- DeepSeek-V3.1: —

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Vision | 1321 | — |
| Blueprint-Bench 2 | 51.2% | — |
| Furniture Assembly | 83.3% | — |

## Multilingual

- Claude Opus 5.5: 59.1 (#2)
- DeepSeek-V3.1: 51.6 (#106)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Non-English | 1507 | 1400 |
| LMArena Chinese | 1588 | 1469 |
| LMArena French | 1514 | 1447 |
| LMArena Russian | 1520 | 1405 |
| LMArena Spanish | 1507 | 1431 |
| LMArena German | — | 1411 |
| LMArena Japanese | — | 1378 |
| LMArena Korean | — | 1337 |

## Instruction Following

- Claude Opus 5.5: 80.0 (#3)
- DeepSeek-V3.1: 73.9 (#110)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Instruction Following | 1537 | 1400 |

## Long Context

- Claude Opus 5.5: 47.1 (#19)
- DeepSeek-V3.1: 36.3 (#232)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Longer Query | 1532 | 1422 |
| Fiction.LiveBench | — | 52.8% |

## Writing & Preference

- Claude Opus 5.5: 78.2 (#3)
- DeepSeek-V3.1: 60.3 (#98)

| Benchmark | Claude Opus 5.5 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Text | 1515 | 1420 |
| LMArena Creative Writing | 1533 | 1401 |
| EQ-Bench Creative Writing | 2050 | 1436 |
| LMArena Multi-Turn | 1499 | 1408 |

## FAQ

### Is Claude Opus 5.5 better than DeepSeek-V3.1?

Claude Opus 5.5 is the stronger model overall, scoring 68.6 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 19× 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-V3.1?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Claude Opus 5.5 lists at $4 and $20.

### Is Claude Opus 5.5 or DeepSeek-V3.1 better for coding?

Claude Opus 5.5 scores higher on coding benchmarks: 71.9 versus 40.3 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-V3.1 share?

18 benchmarks have published results for both models. Claude Opus 5.5 has 44 scored results on Noometry and DeepSeek-V3.1 has 27.