# DeepSeek V4 Pro vs Qwen2.5-Coder-32B

> DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 33.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-qwen2-5-coder-32b
- Last updated: 2026-10-11
- Shared benchmarks: 13

## Summary

- They share 13 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 21.2.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 33K.

## Snapshot

| | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 54.3 | 33.4 |
| Rank | 31 | 245 |
| Context | 1M | 33K |
| Input $/M | $0.66 | $0.66 |
| Output $/M | $1.98 | $1 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Pro: 52.4 (#34)
- Qwen2.5-Coder-32B: 22.6 (#333)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1470 | 1276 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,403 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.8 (#58)
- Qwen2.5-Coder-32B: —

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |

## Reasoning

- DeepSeek V4 Pro: 56.5 (#24)
- Qwen2.5-Coder-32B: 21.2 (#225)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1461 | 1251 |
| Epoch Capabilities Index | 155.31 | 119.49 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| ForecastBench | 56.1 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- DeepSeek V4 Pro: 64.8 (#30)
- Qwen2.5-Coder-32B: 33.3 (#204)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1455 | 1251 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#31)
- Qwen2.5-Coder-32B: 33.4 (#203)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1464 | 1221 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multilingual

- DeepSeek V4 Pro: 54.4 (#45)
- Qwen2.5-Coder-32B: 37.8 (#235)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1439 | 1205 |
| LMArena Chinese | 1486 | 1222 |
| LMArena Russian | 1453 | 1228 |
| LMArena French | 1472 | — |
| LMArena German | 1458 | — |
| LMArena Japanese | 1445 | — |
| LMArena Korean | 1447 | — |
| LMArena Spanish | 1458 | — |

## Instruction Following

- DeepSeek V4 Pro: 76.1 (#47)
- Qwen2.5-Coder-32B: 61.4 (#245)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1448 | 1223 |
| LiveBench Instruction Following | — | 58.7% |

## Long Context

- DeepSeek V4 Pro: 45.0 (#51)
- Qwen2.5-Coder-32B: 38.0 (#208)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1458 | 1251 |
| CL-bench Life | 13.5% | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.5 (#46)
- Qwen2.5-Coder-32B: 41.6 (#240)

| Benchmark | DeepSeek V4 Pro | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1451 | 1230 |
| LMArena Creative Writing | 1446 | 1174 |
| LMArena Multi-Turn | 1467 | 1222 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
| LiveBench Language | — | 23.3% |

## FAQ

### Is DeepSeek V4 Pro better than Qwen2.5-Coder-32B?

DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 33.4 on the Noometry Index.

### Which is cheaper, DeepSeek V4 Pro or Qwen2.5-Coder-32B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.

### Is DeepSeek V4 Pro or Qwen2.5-Coder-32B better for coding?

DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Pro does, with 1M tokens against 33K.

### How many benchmarks do DeepSeek V4 Pro and Qwen2.5-Coder-32B share?

13 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Qwen2.5-Coder-32B has 31.