# o3 vs Qwen3 32B

> o3 is the stronger model overall, scoring 47.5 to 39.2 on the Noometry Index. Qwen3 32B costs 2.9× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/o3-vs-qwen3-32b
- Last updated: 2026-10-11
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. o3 scores higher in 9 categories and Qwen3 32B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.6 to 40.0.
- The biggest single-benchmark swing is Aider Polyglot: 81.3% for o3 and 40% for Qwen3 32B.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.

## Snapshot

| | o3 | Qwen3 32B |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 47.5 | 39.2 |
| Rank | 61 | 172 |
| Context | 200K | 131K |
| Input $/M | $2 | $0.70 |
| Output $/M | $8 | $2.80 |
| Weights | Proprietary | Open |

## Coding

- o3: 46.8 (#64)
- Qwen3 32B: 37.7 (#190)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| Aider Polyglot | 81.3% | 40% |
| LMArena Coding | 1408 | 1358 |
| SWE-bench Verified | 62.3% | — |
| SWE-bench Verified (bash only) | 58.4% | — |
| SciCode | — | 35.4% |
| GSO | 8.8% | — |
| WeirdML | 52.4% | — |
| CadEval | 74% | — |
| ALE-Bench | 933.55 | — |

## Agentic & Tool Use

- o3: 34.5 (#44)
- Qwen3 32B: 32.6 (#62)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 63% | 48.7% |
| GDPval | 30.8% | — |
| DeepResearch Bench | 45.2% | — |
| OSWorld | 23% | — |
| LMArena Search | 1144 | — |
| METR Time Horizons | 65.4% | — |

## Reasoning

- o3: 32.0 (#78)
- Qwen3 32B: 20.2 (#241)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 54.9% |
| CritPt | 1.4% | 0.3% |
| Chess Puzzles | 38% | 5% |
| LMArena Hard Prompts | 1402 | 1334 |
| DTBench | 84.8% | 67.5% |
| LMCA | 39.7% | 17.3% |
| Epoch Capabilities Index | 146.86 | 138.51 |
| ARC-AGI-2 | 6.5% | — |
| SimpleBench | 53.1% | — |
| ARC-AGI-1 | 60.8% | — |
| EnigmaEval | 13.1% | — |
| Mystery Game Puzzles | 29% | — |
| ForecastBench | 62.5 | — |

## Math

- o3: 50.2 (#58)
- Qwen3 32B: 39.7 (#99)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.4% | 66.9% |
| LMArena Math | 1426 | 1399 |
| FrontierMath (Tiers 1-3) | 33.3% | — |
| Omni-MATH | 71.4% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 18.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- o3: 54.6 (#52)
- Qwen3 32B: 40.0 (#125)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 81.8% | 65.7% |
| LMArena Expert | 1402 | 1362 |
| Humanity's Last Exam | 20.3% | — |
| SimpleQA Verified | 49.4% | — |
| MMLU-Pro | 85.9% | — |
| Confabulations | 14.4% | — |
| Vectara Hallucination Rate | — | 5.9% |
| GPQA (HELM) | 75.3% | — |

## Multimodal

- o3: 41.4 (#36)
- Qwen3 32B: —

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1214 | — |
| GeoBench | 74% | — |
| VPCT | 52% | — |

## Multilingual

- o3: 51.7 (#105)
- Qwen3 32B: 45.6 (#167)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1401 | 1317 |
| LMArena Chinese | 1437 | 1357 |
| LMArena German | 1420 | 1341 |
| LMArena Russian | 1406 | 1311 |
| LMArena French | 1430 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1370 | — |
| LMArena Spanish | 1395 | — |

## Instruction Following

- o3: 72.8 (#127)
- Qwen3 32B: 68.9 (#179)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1368 | 1305 |
| IFEval | 86.9% | — |

## Long Context

- o3: 53.3 (#6)
- Qwen3 32B: 43.8 (#87)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | 88.9% | 74.2% |
| LMArena Longer Query | 1372 | 1327 |
| CL-bench | 17.8% | — |

## Writing & Preference

- o3: 63.5 (#64)
- Qwen3 32B: 52.9 (#163)

| Benchmark | o3 | Qwen3 32B |
|---|---|---|
| LMArena Text | 1410 | 1340 |
| LMArena Creative Writing | 1359 | 1297 |
| LMArena Multi-Turn | 1405 | 1331 |
| Short-Story Creative Writing | 83.9% | — |
| EQ-Bench Creative Writing | 1676 | — |
| WildBench | 86.1% | — |

## FAQ

### Is o3 better than Qwen3 32B?

o3 is the stronger model overall, scoring 47.5 to 39.2 on the Noometry Index. Qwen3 32B costs 2.9× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

### Which is cheaper, o3 or Qwen3 32B?

Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; o3 lists at $2 and $8.

### Is o3 or Qwen3 32B better for coding?

o3 scores higher on coding benchmarks: 46.8 versus 37.7 in the Noometry coding category.

### Which has the bigger context window?

o3 does, with 200K tokens against 131K.

### How many benchmarks do o3 and Qwen3 32B share?

24 benchmarks have published results for both models. o3 has 63 scored results on Noometry and Qwen3 32B has 26.