Model comparison
o4-mini vs Qwen3 Max
Qwen3 Max is the stronger model overall, scoring 43.7 to 41.6 on the Noometry Index.
Last verified . 30 shared benchmarks.
Alibaba (Qwen)Summary
- They share 30 benchmarks with published results for both. o4-mini scores higher in 4 categories and Qwen3 Max in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3 Max leads 62.4 to 54.0.
- The biggest single-benchmark swing is SimpleQA Verified: 19.6% for o4-mini and 48.7% for Qwen3 Max.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- Qwen3 Max accepts more context: 262K tokens versus 200K.
Side by side
| o4-mini | Qwen3 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 43.7 |
| Released | 2025-04-16 | 2025-09-23 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $1.10 | $1.20 |
| Output $ / M tokens | $4.40 | $6 |
| Results tracked | 60 | 33 |
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Category by category
Coding Qwen3 Max leads
o4-mini: 40.9 (#127), Qwen3 Max: 43.0 (#93)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1368 | 1456 |
| ALE-Bench | 826.17 | 370.45 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| CadEval | 62% | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Not comparable
o4-mini: 32.6 (#61), Qwen3 Max: —
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
| Vending-Bench 2 | — | 71.56 |
Reasoning o4-mini leads
o4-mini: 24.6 (#162), Qwen3 Max: 22.6 (#190)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 72.5% |
| Chess Puzzles | 26% | 4% |
| LMArena Hard Prompts | 1351 | 1448 |
| Mystery Game Puzzles | 5% | 5% |
| DTBench | 77.6% | 82.1% |
| LMCA | 26.5% | 28.3% |
| Epoch Capabilities Index | 145.64 | 142.38 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| NYT Connections (extended) | — | 30.1% |
| ARC-AGI-1 | 58.7% | — |
| CritPt | 0.6% | — |
| EnigmaEval | 9.2% | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.8 (#89), Qwen3 Max: 38.7 (#131)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.1% | 18.9% |
| OTIS Mock AIME 2024-2025 | 81.7% | 73.3% |
| LMArena Math | 1389 | 1446 |
| MATH Level 5 | 97.8% | 97.1% |
| FrontierMath Tier 4 | 4.9% | — |
| Omni-MATH | 72% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Qwen3 Max leads
o4-mini: 43.6 (#91), Qwen3 Max: 48.1 (#78)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 79.6% | 72.6% |
| SimpleQA Verified | 19.6% | 48.7% |
| LMArena Expert | 1343 | 1455 |
| Humanity's Last Exam | 18.1% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3 Max: —
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Qwen3 Max leads
o4-mini: 47.0 (#154), Qwen3 Max: 53.7 (#62)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1337 | 1429 |
| LMArena Chinese | 1354 | 1478 |
| LMArena French | 1364 | 1449 |
| LMArena German | 1336 | 1463 |
| LMArena Japanese | 1308 | 1397 |
| LMArena Korean | 1312 | 1399 |
| LMArena Russian | 1334 | 1428 |
| LMArena Spanish | 1347 | 1462 |
Instruction Following Too close to call
o4-mini: 75.2 (#68), Qwen3 Max: 74.8 (#87)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1321 | 1419 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3 Max: 41.6 (#134)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| Fiction.LiveBench | 77.8% | 66.7% |
| LMArena Longer Query | 1315 | 1438 |
| CL-bench | — | 14.5% |
Writing & Preference Qwen3 Max leads
o4-mini: 54.0 (#152), Qwen3 Max: 62.4 (#76)
| Benchmark | o4-mini | Qwen3 Max |
|---|---|---|
| LMArena Text | 1353 | 1439 |
| LMArena Creative Writing | 1294 | 1402 |
| LMArena Multi-Turn | 1350 | 1446 |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Qwen3 Max?
Qwen3 Max is the stronger model overall, scoring 43.7 to 41.6 on the Noometry Index.
Which is cheaper, o4-mini or Qwen3 Max?
o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; Qwen3 Max lists at $1.20 and $6.
Is o4-mini or Qwen3 Max better for coding?
Qwen3 Max scores higher on coding benchmarks: 43.0 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3 Max does, with 262K tokens against 200K.
How many benchmarks do o4-mini and Qwen3 Max share?
30 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3 Max has 33.