# Qwen2.5-Coder-32B vs Qwen3 235B-A22B

> Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 1.6× 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/qwen2-5-coder-32b-vs-qwen3-235b-a22b
- Last updated: 2026-10-12
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. Qwen2.5-Coder-32B scores higher in 1 category and Qwen3 235B-A22B in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3 235B-A22B leads 44.3 to 22.6.
- The biggest single-benchmark swing is Aider Polyglot: 16.4% for Qwen2.5-Coder-32B and 59.6% for Qwen3 235B-A22B.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 33K.

## Snapshot

| | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 33.4 | 43.5 |
| Rank | 245 | 91 |
| Context | 33K | 131K |
| Input $/M | $0.66 | $0.70 |
| Output $/M | $1 | $2.80 |
| Weights | Open | Open |

## Coding

- Qwen2.5-Coder-32B: 22.6 (#333)
- Qwen3 235B-A22B: 44.3 (#75)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| Aider Polyglot | 16.4% | 59.6% |
| LMArena Coding | 1276 | 1445 |
| SWE-bench Verified (bash only) | 9% | — |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
| BigCodeBench Instruct | 49% | — |
| LiveBench Coding | 56.9% | — |
| BigCodeBench Complete | 58% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 77% | — |

## Agentic & Tool Use

- Qwen2.5-Coder-32B: —
- Qwen3 235B-A22B: 33.9 (#51)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |

## Reasoning

- Qwen2.5-Coder-32B: 21.2 (#225)
- Qwen3 235B-A22B: 15.7 (#311)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1251 | 1433 |
| Epoch Capabilities Index | 119.49 | 143.85 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 42.1% | — |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LiveBench Data Analysis | 49.9% | — |
| LMCA | — | 29.3% |
| ForecastBench | — | 59.7 |
| HellaSwag | 83% | — |
| LiveBench | 46.2% | — |
| WinoGrande | 80.8% | — |

## Math

- Qwen2.5-Coder-32B: 33.3 (#204)
- Qwen3 235B-A22B: 50.4 (#57)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1251 | 1432 |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| LiveBench Math | 46.6% | — |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
| GSM8K | 93% | — |

## Knowledge

- Qwen2.5-Coder-32B: 33.4 (#203)
- Qwen3 235B-A22B: 49.6 (#73)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Expert | 1221 | 1463 |
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| ARC (AI2) Challenge | 70.5% | — |
| MMLU | 79.1% | — |

## Multilingual

- Qwen2.5-Coder-32B: 37.8 (#235)
- Qwen3 235B-A22B: 52.3 (#89)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1205 | 1409 |
| LMArena Chinese | 1222 | 1481 |
| LMArena Russian | 1228 | 1411 |
| LMArena French | — | 1445 |
| LMArena German | — | 1433 |
| LMArena Japanese | — | 1399 |
| LMArena Korean | — | 1391 |
| LMArena Spanish | — | 1430 |

## Instruction Following

- Qwen2.5-Coder-32B: 61.4 (#245)
- Qwen3 235B-A22B: 72.6 (#136)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1223 | 1408 |
| LiveBench Instruction Following | 58.7% | — |
| IFEval | — | 83.5% |

## Long Context

- Qwen2.5-Coder-32B: 38.0 (#208)
- Qwen3 235B-A22B: 46.1 (#26)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1251 | 1426 |
| Fiction.LiveBench | — | 75% |

## Writing & Preference

- Qwen2.5-Coder-32B: 41.6 (#240)
- Qwen3 235B-A22B: 59.6 (#108)

| Benchmark | Qwen2.5-Coder-32B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1230 | 1419 |
| LMArena Creative Writing | 1174 | 1384 |
| LMArena Multi-Turn | 1222 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
| LiveBench Language | 23.3% | — |

## FAQ

### Is Qwen2.5-Coder-32B better than Qwen3 235B-A22B?

Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 1.6× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.

### Which is cheaper, Qwen2.5-Coder-32B or Qwen3 235B-A22B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.

### Is Qwen2.5-Coder-32B or Qwen3 235B-A22B better for coding?

Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

Qwen3 235B-A22B does, with 131K tokens against 33K.

### How many benchmarks do Qwen2.5-Coder-32B and Qwen3 235B-A22B share?

14 benchmarks have published results for both models. Qwen2.5-Coder-32B has 31 scored results on Noometry and Qwen3 235B-A22B has 49.