Model comparison
o4-mini vs Qwen3-30B-A3B
o4-mini is the stronger model overall, scoring 41.6 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 9.0× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
Alibaba (Qwen)Summary
- They share 30 benchmarks with published results for both. o4-mini scores higher in 7 categories and Qwen3-30B-A3B in 2 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where o4-mini leads 45.5 to 31.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 77.8% for o4-mini and 40.6% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 38.9 |
| Released | 2025-04-16 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 200K | 41K |
| Max output | 100K | 16K |
| Input $ / M tokens | $1.10 | $0.12 |
| Output $ / M tokens | $4.40 | $0.50 |
| Results tracked | 60 | 32 |
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Category by category
Coding o4-mini leads
o4-mini: 40.9 (#127), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| WeirdML | 52.6% | 29.8% |
| LMArena Coding | 1368 | 1416 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| SciCode | — | 33.3% |
| GSO | 3.6% | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use o4-mini leads
o4-mini: 32.6 (#61), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | 41.4% |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning o4-mini leads
o4-mini: 24.6 (#162), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 54.9% |
| CritPt | 0.6% | 0.3% |
| Chess Puzzles | 26% | 8% |
| LMArena Hard Prompts | 1351 | 1398 |
| DTBench | 77.6% | 69.3% |
| LMCA | 26.5% | 22.4% |
| Epoch Capabilities Index | 145.64 | 139.63 |
| 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), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.7% | 70.3% |
| LMArena Math | 1389 | 1394 |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| 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), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 79.6% | 70.1% |
| Confabulations | 15.8% | 12.3% |
| LMArena Expert | 1343 | 1396 |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3-30B-A3B: —
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Qwen3-30B-A3B leads
o4-mini: 47.0 (#154), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1337 | 1372 |
| LMArena Chinese | 1354 | 1433 |
| LMArena French | 1364 | 1418 |
| LMArena German | 1336 | 1380 |
| LMArena Japanese | 1308 | 1337 |
| LMArena Korean | 1312 | 1331 |
| LMArena Russian | 1334 | 1370 |
| LMArena Spanish | 1347 | 1404 |
Instruction Following o4-mini leads
o4-mini: 75.2 (#68), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1363 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 77.8% | 40.6% |
| LMArena Longer Query | 1315 | 1379 |
Writing & Preference Qwen3-30B-A3B leads
o4-mini: 54.0 (#152), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | o4-mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1353 | 1384 |
| LMArena Creative Writing | 1294 | 1317 |
| Short-Story Creative Writing | 75% | 75.3% |
| LMArena Multi-Turn | 1350 | 1378 |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Qwen3-30B-A3B?
o4-mini is the stronger model overall, scoring 41.6 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 9.0× 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 Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3-30B-A3B better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 41K.
How many benchmarks do o4-mini and Qwen3-30B-A3B share?
30 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3-30B-A3B has 32.