# o4-mini vs Qwen2.5-Coder-32B

> o4-mini is the stronger model overall, scoring 41.6 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.6× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/o4-mini-vs-qwen2-5-coder-32b
- Last updated: 2026-10-11
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. o4-mini 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 coding, where o4-mini leads 40.9 to 22.6.
- The biggest single-benchmark swing is Aider Polyglot: 72% for o4-mini and 16.4% for Qwen2.5-Coder-32B.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

## Snapshot

| | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 33.4 |
| Rank | 132 | 245 |
| Context | 200K | 33K |
| Input $/M | $1.10 | $0.66 |
| Output $/M | $4.40 | $1 |
| Weights | Proprietary | Open |

## Coding

- o4-mini: 40.9 (#127)
- Qwen2.5-Coder-32B: 22.6 (#333)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | 45% | 9% |
| Aider Polyglot | 72% | 16.4% |
| LMArena Coding | 1368 | 1276 |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- o4-mini: 32.6 (#61)
- Qwen2.5-Coder-32B: —

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |

## Reasoning

- o4-mini: 24.6 (#162)
- Qwen2.5-Coder-32B: 21.2 (#225)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1351 | 1251 |
| Epoch Capabilities Index | 145.64 | 119.49 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| Kagi LLM Benchmark | 67.6% | — |
| ARC-AGI-1 | 58.7% | — |
| CritPt | 0.6% | — |
| Chess Puzzles | 26% | — |
| EnigmaEval | 9.2% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 5% | — |
| DTBench | 77.6% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 26.5% | — |
| ForecastBench | 61.8 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- o4-mini: 40.8 (#89)
- Qwen2.5-Coder-32B: 33.3 (#204)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1389 | 1251 |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 81.7% | — |
| Omni-MATH | 72% | — |
| LiveBench Math | — | 46.6% |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
| GSM8K | — | 93% |

## Knowledge

- o4-mini: 43.6 (#91)
- Qwen2.5-Coder-32B: 33.4 (#203)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1343 | 1221 |
| GPQA Diamond | 79.6% | — |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multimodal

- o4-mini: 40.2 (#49)
- Qwen2.5-Coder-32B: —

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |

## Multilingual

- o4-mini: 47.0 (#154)
- Qwen2.5-Coder-32B: 37.8 (#235)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1337 | 1205 |
| LMArena Chinese | 1354 | 1222 |
| LMArena Russian | 1334 | 1228 |
| LMArena French | 1364 | — |
| LMArena German | 1336 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1312 | — |
| LMArena Spanish | 1347 | — |

## Instruction Following

- o4-mini: 75.2 (#68)
- Qwen2.5-Coder-32B: 61.4 (#245)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 92.8% | — |

## Long Context

- o4-mini: 45.5 (#33)
- Qwen2.5-Coder-32B: 38.0 (#208)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1315 | 1251 |
| Fiction.LiveBench | 77.8% | — |

## Writing & Preference

- o4-mini: 54.0 (#152)
- Qwen2.5-Coder-32B: 41.6 (#240)

| Benchmark | o4-mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1353 | 1230 |
| LMArena Creative Writing | 1294 | 1174 |
| LMArena Multi-Turn | 1350 | 1222 |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
| LiveBench Language | — | 23.3% |

## FAQ

### Is o4-mini better than Qwen2.5-Coder-32B?

o4-mini is the stronger model overall, scoring 41.6 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.6× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.

### Which is cheaper, o4-mini 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; o4-mini lists at $1.10 and $4.40.

### Is o4-mini or Qwen2.5-Coder-32B better for coding?

o4-mini scores higher on coding benchmarks: 40.9 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

o4-mini does, with 200K tokens against 33K.

### How many benchmarks do o4-mini and Qwen2.5-Coder-32B share?

15 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen2.5-Coder-32B has 31.