Model comparison
o4-mini vs Qwen3 32B
o4-mini is the stronger model overall, scoring 41.6 to 39.2 on the Noometry Index. Qwen3 32B costs 1.6× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. o4-mini scores higher in 8 categories and Qwen3 32B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in instruction following, where o4-mini leads 75.2 to 68.9.
- The biggest single-benchmark swing is Aider Polyglot: 72% for o4-mini and 40% for Qwen3 32B.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 39.2 |
| Released | 2025-04-16 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 16K |
| Input $ / M tokens | $1.10 | $0.70 |
| Output $ / M tokens | $4.40 | $2.80 |
| Results tracked | 60 | 26 |
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Category by category
Coding o4-mini leads
o4-mini: 40.9 (#127), Qwen3 32B: 37.7 (#190)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| Aider Polyglot | 72% | 40% |
| LMArena Coding | 1368 | 1358 |
| SWE-bench Verified (bash only) | 45% | — |
| SciCode | — | 35.4% |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Too close to call
o4-mini: 32.6 (#61), Qwen3 32B: 32.6 (#62)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | 48.7% |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning o4-mini leads
o4-mini: 24.6 (#162), Qwen3 32B: 20.2 (#241)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 54.9% |
| CritPt | 0.6% | 0.3% |
| Chess Puzzles | 26% | 5% |
| LMArena Hard Prompts | 1351 | 1334 |
| DTBench | 77.6% | 67.5% |
| LMCA | 26.5% | 17.3% |
| Epoch Capabilities Index | 145.64 | 138.51 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| ARC-AGI-1 | 58.7% | — |
| EnigmaEval | 9.2% | — |
| Mystery Game Puzzles | 5% | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.8 (#89), Qwen3 32B: 39.7 (#99)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.7% | 66.9% |
| LMArena Math | 1389 | 1399 |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| Omni-MATH | 72% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge o4-mini leads
o4-mini: 43.6 (#91), Qwen3 32B: 40.0 (#125)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 79.6% | 65.7% |
| Vectara Hallucination Rate | 18.6% | 5.9% |
| LMArena Expert | 1343 | 1362 |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3 32B: —
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual o4-mini leads
o4-mini: 47.0 (#154), Qwen3 32B: 45.6 (#167)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1337 | 1317 |
| LMArena Chinese | 1354 | 1357 |
| LMArena German | 1336 | 1341 |
| LMArena Russian | 1334 | 1311 |
| LMArena French | 1364 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1312 | — |
| LMArena Spanish | 1347 | — |
Instruction Following o4-mini leads
o4-mini: 75.2 (#68), Qwen3 32B: 68.9 (#179)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1305 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3 32B: 43.8 (#87)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | 77.8% | 74.2% |
| LMArena Longer Query | 1315 | 1327 |
Writing & Preference o4-mini leads
o4-mini: 54.0 (#152), Qwen3 32B: 52.9 (#163)
| Benchmark | o4-mini | Qwen3 32B |
|---|---|---|
| LMArena Text | 1353 | 1340 |
| LMArena Creative Writing | 1294 | 1297 |
| LMArena Multi-Turn | 1350 | 1331 |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Qwen3 32B?
o4-mini is the stronger model overall, scoring 41.6 to 39.2 on the Noometry Index. Qwen3 32B costs 1.6× 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 Qwen3 32B?
Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3 32B better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 37.7 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 131K.
How many benchmarks do o4-mini and Qwen3 32B share?
25 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3 32B has 26.