# Claude Opus 4.7 vs DeepSeek-V3.1

> Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 24× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-7-vs-deepseek-v3-1
- Last updated: 2026-10-12
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. Claude Opus 4.7 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 4.7 leads 66.7 to 38.9.
- The biggest single-benchmark swing is WeirdML: 76.4% for Claude Opus 4.7 and 38.4% for DeepSeek-V3.1.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
- Claude Opus 4.7 accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 58.3 | 42.8 |
| Rank | 19 | 108 |
| Context | 1M | 164K |
| Input $/M | $5 | $0.25 |
| Output $/M | $25 | $0.95 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4.7: 59.6 (#13)
- DeepSeek-V3.1: 40.3 (#144)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| WeirdML | 76.4% | 38.4% |
| LMArena Coding | 1518 | 1417 |
| SWE-bench Verified | 83.5% | — |
| FrontierCode | 38.5% | — |
| LMArena WebDev | 1558 | — |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| MirrorCode | 31.1% | — |
| ALE-Bench | 1,323 | — |

## Agentic & Tool Use

- Claude Opus 4.7: 47.9 (#10)
- DeepSeek-V3.1: —

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |

## Reasoning

- Claude Opus 4.7: 53.8 (#29)
- DeepSeek-V3.1: 27.9 (#110)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| SimpleBench | 61.7% | 40% |
| Kagi LLM Benchmark | 80.7% | 53.2% |
| LMArena Hard Prompts | 1506 | 1417 |
| DTBench | 94.7% | 82.7% |
| LMCA | 52.2% | 24.3% |
| Epoch Capabilities Index | 156.25 | 139.92 |
| ForecastBench | 60.3 | 58 |
| ARC-AGI-2 | 75.8% | — |
| NYT Connections (extended) | 39% | — |
| ARC-AGI-1 | 93.5% | — |
| CritPt | 12% | — |
| Chess Puzzles | 30% | — |
| Thematic Generalization | 72.8% | — |
| EBR-Bench | 19% | — |
| Mystery Game Puzzles | 28% | — |

## Math

- Claude Opus 4.7: 66.7 (#26)
- DeepSeek-V3.1: 38.9 (#122)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Math | 1499 | 1420 |
| FrontierMath (Tiers 1-3) | 70.2% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 73.6% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 54% | — |
| FrontierMath (Feb 2025 set) | 43.8% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |

## Knowledge

- Claude Opus 4.7: 62.6 (#23)
- DeepSeek-V3.1: 43.7 (#90)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| Vectara Hallucination Rate | 12% | 5.5% |
| LMArena Expert | 1521 | 1405 |
| GPQA Diamond | 90.2% | — |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 51.7% | — |

## Multimodal

- Claude Opus 4.7: 41.2 (#38)
- DeepSeek-V3.1: —

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Vision | 1316 | — |
| Blueprint-Bench 2 | 24.5% | — |
| Furniture Assembly | 33.3% | — |
| LMArena Document | 1495 | — |

## Multilingual

- Claude Opus 4.7: 57.3 (#10)
- DeepSeek-V3.1: 51.6 (#106)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Non-English | 1480 | 1400 |
| LMArena Chinese | 1531 | 1469 |
| LMArena French | 1503 | 1447 |
| LMArena German | 1495 | 1411 |
| LMArena Japanese | 1472 | 1378 |
| LMArena Korean | 1464 | 1337 |
| LMArena Russian | 1494 | 1405 |
| LMArena Spanish | 1495 | 1431 |

## Instruction Following

- Claude Opus 4.7: 78.4 (#10)
- DeepSeek-V3.1: 73.9 (#110)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Instruction Following | 1498 | 1400 |

## Long Context

- Claude Opus 4.7: 46.2 (#25)
- DeepSeek-V3.1: 36.3 (#232)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Longer Query | 1505 | 1422 |
| Fiction.LiveBench | — | 52.8% |

## Writing & Preference

- Claude Opus 4.7: 75.1 (#8)
- DeepSeek-V3.1: 60.3 (#98)

| Benchmark | Claude Opus 4.7 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Text | 1490 | 1420 |
| LMArena Creative Writing | 1486 | 1401 |
| EQ-Bench Creative Writing | 1914 | 1436 |
| LMArena Multi-Turn | 1505 | 1408 |
| EQ-Bench 4 | 1311 | — |

## FAQ

### Is Claude Opus 4.7 better than DeepSeek-V3.1?

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 24× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4.7 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 4.7 lists at $5 and $25.

### Is Claude Opus 4.7 or DeepSeek-V3.1 better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 40.3 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 164K.

### How many benchmarks do Claude Opus 4.7 and DeepSeek-V3.1 share?

26 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and DeepSeek-V3.1 has 27.