# DeepSeek-V3 vs Qwen3.5-9B

> DeepSeek-V3 is the stronger model overall, scoring 39.2 to 33.3 on the Noometry Index. Qwen3.5-9B costs 3.6× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-qwen3-5-9b
- Last updated: 2026-10-10
- Shared benchmarks: 7

## Summary

- They share 7 benchmarks with published results for both. DeepSeek-V3 scores higher in 1 category and Qwen3.5-9B in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5-9B leads 46.0 to 37.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- Qwen3.5-9B accepts more context: 262K tokens versus 164K.

## Snapshot

| | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 33.3 |
| Rank | 157 | 235 |
| Context | 164K | 262K |
| Input $/M | $0.24 | $0.10 |
| Output $/M | $0.90 | $0.15 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3: 41.9 (#99)
- Qwen3.5-9B: 35.3 (#213)

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| SciCode | 35.8% | 27.5% |
| Aider Polyglot | 55.1% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| LMArena Coding | 1368 | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Qwen3.5-9B: 14.4 (#143)

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- Qwen3.5-9B: 22.5 (#178)

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 64.8% | 71.2% |
| LMCA | 15.5% | 24.5% |
| Epoch Capabilities Index | 135.95 | 139.45 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 65.8% | — |
| LMArena Hard Prompts | 1365 | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 30.4 (#208)
- Qwen3.5-9B: 33.5 (#174)

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| LMArena Math | 1373 | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- Qwen3.5-9B: 46.0 (#77)

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 67.6% | 79% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| LMArena Expert | 1352 | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- Qwen3.5-9B: —

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1358 | — |
| LMArena Chinese | 1390 | — |
| LMArena French | 1386 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1321 | — |
| LMArena Russian | 1372 | — |
| LMArena Spanish | 1358 | — |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- Qwen3.5-9B: —

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
| LMArena Instruction Following | 1345 | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- Qwen3.5-9B: —

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 50% | — |
| LMArena Longer Query | 1353 | — |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- Qwen3.5-9B: —

| Benchmark | DeepSeek-V3 | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1375 | — |
| LMArena Creative Writing | 1364 | — |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LMArena Multi-Turn | 1389 | — |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Qwen3.5-9B?

DeepSeek-V3 is the stronger model overall, scoring 39.2 to 33.3 on the Noometry Index. Qwen3.5-9B costs 3.6× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or Qwen3.5-9B?

Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.

### Is DeepSeek-V3 or Qwen3.5-9B better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 35.3 in the Noometry coding category.

### Which has the bigger context window?

Qwen3.5-9B does, with 262K tokens against 164K.

### How many benchmarks do DeepSeek-V3 and Qwen3.5-9B share?

7 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3.5-9B has 10.