# DeepSeek-V3 vs Qwen3.7 Max

> Qwen3.7 Max is the stronger model overall, scoring 51.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 9.3× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-qwen3-7-max
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Qwen3.7 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.3 to 30.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 95.6% for Qwen3.7 Max.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.2 | 51.4 |
| Rank | 157 | 35 |
| Context | 164K | 1M |
| Input $/M | $0.24 | $2.50 |
| Output $/M | $0.90 | $7.50 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 41.9 (#99)
- Qwen3.7 Max: 50.2 (#43)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| SciCode | 35.8% | 48.8% |
| LMArena Coding | 1368 | 1498 |
| SWE-bench Verified | — | 77.3% |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1515 |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 1,189 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Qwen3.7 Max: 21.8 (#129)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| GBAEval | — | 0.4% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- Qwen3.7 Max: 48.4 (#36)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 27.2% | 70.4% |
| CritPt | 0% | 13.4% |
| LMArena Hard Prompts | 1365 | 1483 |
| DTBench | 64.8% | 92.3% |
| LMCA | 15.5% | 44% |
| Epoch Capabilities Index | 135.95 | 153.74 |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 19% |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 32% |
| 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)
- Qwen3.7 Max: 62.3 (#26)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 95.6% |
| LMArena Math | 1373 | 1488 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| ProofBench | — | 26% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- Qwen3.7 Max: 61.7 (#24)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 67.6% | 90.9% |
| LMArena Expert | 1352 | 1488 |
| SimpleQA Verified | — | 55.8% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- Qwen3.7 Max: 57.1 (#13)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1358 | 1475 |
| LMArena Chinese | 1390 | 1533 |
| LMArena Russian | 1372 | 1482 |
| LMArena French | 1386 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1321 | — |
| LMArena Spanish | 1358 | — |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- Qwen3.7 Max: 76.9 (#34)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1345 | 1460 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- Qwen3.7 Max: 45.4 (#34)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1353 | 1484 |
| Fiction.LiveBench | 50% | — |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- Qwen3.7 Max: 65.2 (#52)

| Benchmark | DeepSeek-V3 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1375 | 1476 |
| LMArena Creative Writing | 1364 | 1450 |
| LMArena Multi-Turn | 1389 | 1482 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| EQ-Bench 4 | — | 1110 |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Qwen3.7 Max?

Qwen3.7 Max is the stronger model overall, scoring 51.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 9.3× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or Qwen3.7 Max?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.

### Is DeepSeek-V3 or Qwen3.7 Max better for coding?

Qwen3.7 Max scores higher on coding benchmarks: 50.2 versus 41.9 in the Noometry coding category.

### Which has the bigger context window?

Qwen3.7 Max does, with 1M tokens against 164K.

### How many benchmarks do DeepSeek-V3 and Qwen3.7 Max share?

20 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen3.7 Max has 33.