# DeepSeek-V3.1 vs Qwen1.5-7B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-qwen1-5-7b
- Last updated: 2026-10-12
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Qwen1.5-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 29.6.

## Snapshot

| | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.8 | 31.4 |
| Rank | 108 | 273 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Qwen1.5-7B: 32.2 (#276)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Coding | 1417 | 1107 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Qwen1.5-7B: 20.4 (#240)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1065 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Qwen1.5-7B: 31.4 (#224)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Math | 1420 | 1080 |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Qwen1.5-7B: 28.7 (#243)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Expert | 1405 | 1055 |
| Vectara Hallucination Rate | 5.5% | — |
| MMLU | — | 62.6% |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Qwen1.5-7B: 28.5 (#271)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Non-English | 1400 | 1058 |
| LMArena Chinese | 1469 | 1141 |
| LMArena Russian | 1405 | 1006 |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Qwen1.5-7B: 54.1 (#281)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1058 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Qwen1.5-7B: 33.1 (#266)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Longer Query | 1422 | 1090 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Qwen1.5-7B: 29.6 (#293)

| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Text | 1420 | 1083 |
| LMArena Creative Writing | 1401 | 1035 |
| LMArena Multi-Turn | 1408 | 1062 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Qwen1.5-7B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.4 on the Noometry Index.

### Is DeepSeek-V3.1 or Qwen1.5-7B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 32.2 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Qwen1.5-7B share?

12 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen1.5-7B has 13.