# DeepSeek-V3 vs Qwen3 235B-A22B

> Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 39.5 on the Noometry Index. DeepSeek-V3 costs 3.0× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-qwen3-235b-a22b
- Last updated: 2026-10-12
- Shared benchmarks: 41

## Summary

- They share 41 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and Qwen3 235B-A22B in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 235B-A22B leads 50.4 to 32.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 86.7% for Qwen3 235B-A22B.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- DeepSeek-V3 accepts more context: 164K tokens versus 131K.

## Snapshot

| | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.5 | 43.5 |
| Rank | 166 | 91 |
| Context | 164K | 131K |
| Input $/M | $0.24 | $0.70 |
| Output $/M | $0.90 | $2.80 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3: 42.3 (#106)
- Qwen3 235B-A22B: 44.3 (#75)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| Aider Polyglot | 55.1% | 59.6% |
| SciCode | 35.8% | 42.4% |
| WeirdML | 36.1% | 41% |
| LMArena Coding | 1368 | 1445 |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Qwen3 235B-A22B: 33.9 (#51)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| METR Time Horizons | 49.6% | — |
| Vending-Bench 2 | — | -11.34 |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- Qwen3 235B-A22B: 15.7 (#311)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| SimpleBench | 27.2% | 31% |
| Kagi LLM Benchmark | 52.3% | 69.4% |
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1365 | 1433 |
| DTBench | 64.8% | 80.3% |
| LMCA | 15.5% | 29.3% |
| Epoch Capabilities Index | 135.94 | 143.85 |
| ForecastBench | 59.1 | 59.7 |
| ARC-AGI-2 | — | 1.3% |
| ARC-AGI-1 | — | 11% |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 9% |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- Qwen3 235B-A22B: 50.4 (#57)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 86.7% |
| Omni-MATH | 40.3% | 71.8% |
| LMArena Math | 1373 | 1432 |
| MATH Level 5 | 75.5% | 68.9% |
| FrontierMath (Feb 2025 set) | 1.7% | 8.5% |
| LiveBench Math | 73.5% | — |
| FrontierMath Tier 4 (v1) | — | 0% |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- Qwen3 235B-A22B: 49.6 (#73)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 67.6% | 80.1% |
| MMLU-Pro | 72.3% | 84.4% |
| Confabulations | 26.1% | 15.6% |
| Vectara Hallucination Rate | 6.1% | 9.3% |
| GPQA (HELM) | 53.8% | 72.7% |
| LMArena Expert | 1351 | 1463 |
| SimpleQA Verified | — | 40.4% |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- Qwen3 235B-A22B: 52.3 (#89)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1358 | 1409 |
| LMArena Chinese | 1391 | 1481 |
| LMArena French | 1385 | 1445 |
| LMArena German | 1374 | 1433 |
| LMArena Japanese | 1333 | 1399 |
| LMArena Korean | 1319 | 1391 |
| LMArena Russian | 1373 | 1411 |
| LMArena Spanish | 1358 | 1430 |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- Qwen3 235B-A22B: 72.6 (#136)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| IFEval | 83.2% | 83.5% |
| LMArena Instruction Following | 1345 | 1408 |
| LiveBench Instruction Following | 81.5% | — |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- Qwen3 235B-A22B: 46.1 (#26)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | 50% | 75% |
| LMArena Longer Query | 1352 | 1426 |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- Qwen3 235B-A22B: 59.6 (#108)

| Benchmark | DeepSeek-V3 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1375 | 1419 |
| LMArena Creative Writing | 1364 | 1384 |
| Short-Story Creative Writing | 77% | 83% |
| EQ-Bench Creative Writing | 1472 | 1366 |
| WildBench | 83% | 86.6% |
| LMArena Multi-Turn | 1389 | 1432 |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Qwen3 235B-A22B?

Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 39.5 on the Noometry Index. DeepSeek-V3 costs 3.0× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or Qwen3 235B-A22B?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.

### Is DeepSeek-V3 or Qwen3 235B-A22B better for coding?

Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 42.3 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3 does, with 164K tokens against 131K.

### How many benchmarks do DeepSeek-V3 and Qwen3 235B-A22B share?

41 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3 235B-A22B has 49.