# DeepSeek-V3.2-Exp vs Qwen2.5-Max

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 40.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-qwen2-5-max
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 7 categories and Qwen2.5-Max in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 34.8.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.9 | 40.1 |
| Rank | 68 | 142 |
| Context | 164K | — |
| Input $/M | $0.26 | — |
| Output $/M | $0.38 | — |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- Qwen2.5-Max: 41.2 (#118)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Coding | 1454 | 1359 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LiveBench Coding | — | 64.4% |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- Qwen2.5-Max: —

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- Qwen2.5-Max: 24.6 (#144)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1360 |
| Epoch Capabilities Index | 146.28 | 132.54 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LiveBench Reasoning | — | 51.4% |
| DTBench | 87.7% | — |
| LiveBench Data Analysis | — | 67.9% |
| LMCA | 29.1% | — |
| LiveBench | — | 62.3% |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- Qwen2.5-Max: 34.3 (#162)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Math | 1435 | 1369 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LiveBench Math | — | 58.4% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- Qwen2.5-Max: 34.8 (#181)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Expert | 1436 | 1337 |
| GPQA Diamond | 83.4% | — |
| Confabulations | — | 21.8% |
| Vectara Hallucination Rate | 5.3% | — |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- Qwen2.5-Max: 48.3 (#140)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Non-English | 1409 | 1352 |
| LMArena Chinese | 1461 | 1382 |
| LMArena French | 1435 | 1396 |
| LMArena German | 1440 | 1350 |
| LMArena Japanese | 1377 | 1299 |
| LMArena Korean | 1372 | 1304 |
| LMArena Russian | 1423 | 1353 |
| LMArena Spanish | 1441 | 1377 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- Qwen2.5-Max: 71.5 (#147)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Instruction Following | 1413 | 1335 |
| LiveBench Instruction Following | — | 75.3% |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- Qwen2.5-Max: 41.3 (#137)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Longer Query | 1428 | 1358 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- Qwen2.5-Max: 55.6 (#141)

| Benchmark | DeepSeek-V3.2-Exp | Qwen2.5-Max |
|---|---|---|
| LMArena Text | 1425 | 1367 |
| LMArena Creative Writing | 1403 | 1339 |
| LMArena Multi-Turn | 1427 | 1364 |
| Short-Story Creative Writing | — | 72.9% |
| EQ-Bench Creative Writing | 1515 | — |
| LiveBench Language | — | 56.3% |

## FAQ

### Is DeepSeek-V3.2-Exp better than Qwen2.5-Max?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 40.1 on the Noometry Index.

### Is DeepSeek-V3.2-Exp or Qwen2.5-Max better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 41.2 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.2-Exp and Qwen2.5-Max share?

18 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Qwen2.5-Max has 27.