# DeepSeek-V2.5 (Sep 2024) vs Qwen3 14B

> DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 35.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v2-5-vs-qwen3-14b
- Last updated: 2026-10-12
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where DeepSeek-V2.5 (Sep 2024) leads 25.6 to 18.5.

## Snapshot

| | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 37.6 | 35.5 |
| Rank | 200 | 225 |
| Context | — | 131K |
| Input $/M | — | $0.35 |
| Output $/M | — | $1.40 |
| Weights | Open | Open |

## Coding

- DeepSeek-V2.5 (Sep 2024): 31.7 (#281)
- Qwen3 14B: 37.3 (#195)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| Aider Polyglot | 17.8% | — |
| SciCode | — | 31.6% |
| BigCodeBench Instruct | 48.6% | — |
| LMArena Coding | 1309 | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |

## Agentic & Tool Use

- DeepSeek-V2.5 (Sep 2024): —
- Qwen3 14B: 29.6 (#83)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |

## Reasoning

- DeepSeek-V2.5 (Sep 2024): 25.6 (#145)
- Qwen3 14B: 18.5 (#280)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1289 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |

## Math

- DeepSeek-V2.5 (Sep 2024): 35.9 (#177)
- Qwen3 14B: 38.6 (#133)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| LMArena Math | 1288 | — |

## Knowledge

- DeepSeek-V2.5 (Sep 2024): 34.8 (#193)
- Qwen3 14B: 39.3 (#134)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1266 | — |

## Multilingual

- DeepSeek-V2.5 (Sep 2024): 42.5 (#193)
- Qwen3 14B: —

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1273 | — |
| LMArena Chinese | 1318 | — |
| LMArena French | 1289 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Russian | 1289 | — |
| LMArena Spanish | 1248 | — |

## Instruction Following

- DeepSeek-V2.5 (Sep 2024): 67.5 (#194)
- Qwen3 14B: —

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1280 | — |

## Long Context

- DeepSeek-V2.5 (Sep 2024): 39.5 (#174)
- Qwen3 14B: 38.1 (#204)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1301 | — |

## Writing & Preference

- DeepSeek-V2.5 (Sep 2024): 49.8 (#187)
- Qwen3 14B: —

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 14B |
|---|---|---|
| LMArena Text | 1294 | — |
| LMArena Creative Writing | 1285 | — |
| LMArena Multi-Turn | 1297 | — |

## FAQ

### Is DeepSeek-V2.5 (Sep 2024) better than Qwen3 14B?

DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 35.5 on the Noometry Index.

### Is DeepSeek-V2.5 (Sep 2024) or Qwen3 14B better for coding?

Qwen3 14B scores higher on coding benchmarks: 37.3 versus 31.7 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3 14B share?

0 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3 14B has 12.