# DeepSeek-V3.1 vs Mercury 2

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 38.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-mercury-2
- Last updated: 2026-10-10
- Shared benchmarks: 13

## Summary

- They share 13 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and Mercury 2 in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 35.6.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 5.5% for DeepSeek-V3.1 and 12.3% for Mercury 2.
- Mercury 2 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 42.2 | 38.8 |
| Rank | 106 | 167 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $0.25 |
| Output $/M | $0.95 | $0.75 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Mercury 2: 33.2 (#250)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| WeirdML | 38.4% | 43.2% |
| LMArena Coding | 1417 | 1391 |
| LMArena WebDev | — | 1170 |
| SciCode | — | 38.7% |
| ALE-Bench | — | 785.58 |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Mercury 2: 23.0 (#170)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1362 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 0.8% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Mercury 2: —

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Math | 1420 | — |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Mercury 2: 35.6 (#170)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 12.3% |
| LMArena Expert | 1403 | 1358 |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Mercury 2: 46.8 (#153)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1400 | 1331 |
| LMArena Chinese | 1470 | 1416 |
| LMArena Russian | 1405 | 1303 |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1429 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Mercury 2: 70.4 (#161)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1328 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Mercury 2: 40.4 (#149)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1422 | 1329 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Mercury 2: 54.0 (#151)

| Benchmark | DeepSeek-V3.1 | Mercury 2 |
|---|---|---|
| LMArena Text | 1420 | 1355 |
| LMArena Creative Writing | 1401 | 1289 |
| LMArena Multi-Turn | 1408 | 1358 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Mercury 2?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 38.8 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1 or Mercury 2?

Mercury 2 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

### Is DeepSeek-V3.1 or Mercury 2 better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 33.2 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and Mercury 2 share?

13 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mercury 2 has 17.