Model comparison
o3 vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 47.5 on the Noometry Index.
Last verified . 28 shared benchmarks.
Alibaba (Qwen)Summary
- They share 28 benchmarks with published results for both. o3 scores higher in 2 categories and Qwen3.8 Max in 8 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 50.2.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 33.3% for o3 and 74.7% for Qwen3.8 Max.
- Qwen3.8 Max is cheaper at $2 / $6 per million input/output tokens, against $2 / $8 for o3.
- Qwen3.8 Max accepts more context: 1M tokens versus 200K.
Side by side
| o3 | Qwen3.8 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 47.5 | 56.8 |
| Released | 2025-04-16 | 2026-08-02 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 1M |
| Max output | 100K | 131K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $8 | $6 |
| Results tracked | 63 | 39 |
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Category by category
Coding Qwen3.8 Max leads
o3: 46.8 (#64), Qwen3.8 Max: 53.5 (#29)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Coding | 1408 | 1502 |
| SWE-bench Verified | 62.3% | — |
| DeepSWE | — | 57.5% |
| SWE-bench Verified (bash only) | 58.4% | — |
| Aider Polyglot | 81.3% | — |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| SciCode | — | 53.2% |
| GSO | 8.8% | — |
| WeirdML | 52.4% | — |
| CadEval | 74% | — |
| ALE-Bench | 933.55 | — |
Agentic & Tool Use Qwen3.8 Max leads
o3: 34.5 (#44), Qwen3.8 Max: 45.4 (#14)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| Berkeley Function Calling Leaderboard | 63% | — |
| GDPval | 30.8% | — |
| τ²-bench Banking | — | 55.1% |
| DeepResearch Bench | 45.2% | — |
| OSWorld | 23% | — |
| GDP.pdf | — | 23.2% |
| LMArena Search | 1144 | — |
| METR Time Horizons | 65.4% | — |
Reasoning Qwen3.8 Max leads
o3: 32.0 (#78), Qwen3.8 Max: 54.4 (#26)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| CritPt | 1.4% | 20% |
| Chess Puzzles | 38% | 40% |
| LMArena Hard Prompts | 1402 | 1496 |
| Mystery Game Puzzles | 29% | 38% |
| DTBench | 84.8% | 92% |
| LMCA | 39.7% | 46.2% |
| Epoch Capabilities Index | 146.86 | 156.41 |
| ARC-AGI-2 | 6.5% | — |
| SimpleBench | 53.1% | — |
| Kagi LLM Benchmark | 67.6% | — |
| NYT Connections (extended) | — | 88.3% |
| ARC-AGI-1 | 60.8% | — |
| EnigmaEval | 13.1% | — |
| ForecastBench | 62.5 | — |
Math Qwen3.8 Max leads
o3: 50.2 (#58), Qwen3.8 Max: 73.2 (#20)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 33.3% | 74.7% |
| OTIS Mock AIME 2024-2025 | 84.4% | 100% |
| LMArena Math | 1426 | 1499 |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 71.4% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 18.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.8 Max leads
o3: 54.6 (#52), Qwen3.8 Max: 61.7 (#27)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 81.8% | 92.7% |
| SimpleQA Verified | 49.4% | 47.3% |
| LMArena Expert | 1402 | 1507 |
| Humanity's Last Exam | 20.3% | — |
| MMLU-Pro | 85.9% | — |
| Confabulations | 14.4% | — |
| GPQA (HELM) | 75.3% | — |
Multimodal o3 leads
o3: 41.4 (#36), Qwen3.8 Max: 37.2 (#75)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1214 | 1314 |
| GeoBench | 74% | — |
| VPCT | 52% | — |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
o3: 51.7 (#105), Qwen3.8 Max: 56.7 (#18)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1401 | 1472 |
| LMArena Chinese | 1437 | 1538 |
| LMArena French | 1430 | 1503 |
| LMArena German | 1420 | 1483 |
| LMArena Japanese | 1403 | 1467 |
| LMArena Korean | 1370 | 1461 |
| LMArena Russian | 1406 | 1481 |
| LMArena Spanish | 1395 | 1492 |
Instruction Following Qwen3.8 Max leads
o3: 72.8 (#127), Qwen3.8 Max: 77.6 (#17)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1368 | 1479 |
| IFEval | 86.9% | — |
Long Context o3 leads
o3: 53.3 (#6), Qwen3.8 Max: 45.6 (#31)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1372 | 1489 |
| Fiction.LiveBench | 88.9% | — |
| CL-bench | 17.8% | — |
Writing & Preference Qwen3.8 Max leads
o3: 63.5 (#64), Qwen3.8 Max: 67.1 (#30)
| Benchmark | o3 | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1410 | 1483 |
| LMArena Creative Writing | 1359 | 1479 |
| LMArena Multi-Turn | 1405 | 1489 |
| Short-Story Creative Writing | 83.9% | — |
| EQ-Bench Creative Writing | 1676 | — |
| WildBench | 86.1% | — |
Frequently asked questions
Is o3 better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 47.5 on the Noometry Index.
Which is cheaper, o3 or Qwen3.8 Max?
Qwen3.8 Max is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; o3 lists at $2 and $8.
Is o3 or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 46.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 200K.
How many benchmarks do o3 and Qwen3.8 Max share?
28 benchmarks have published results for both models. o3 has 63 scored results on Noometry and Qwen3.8 Max has 39.