Model comparison
o3-mini vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 36.7 on the Noometry Index.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. o3-mini scores higher in 0 categories and Qwen3.8 27B in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-1: 34.5% for o3-mini and 87.5% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- Qwen3.8 27B accepts more context: 262K tokens versus 200K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| o3-mini | Qwen3.8 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 36.7 | 46.0 |
| Released | 2024-12-20 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 33K |
| Input $ / M tokens | $1.10 | $0.99 |
| Output $ / M tokens | $4.40 | $1.49 |
| Results tracked | 51 | 31 |
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Category by category
Coding Qwen3.8 27B leads
o3-mini: 40.8 (#132), Qwen3.8 27B: 50.5 (#44)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| SciCode | 39.8% | 46.6% |
| LMArena Coding | 1378 | 1482 |
| Aider Polyglot | 60.4% | — |
| LMArena WebDev | — | 1593 |
| GSO | 1.3% | — |
| WeirdML | 43.7% | — |
| LiveBench Coding | 82.7% | — |
| CadEval | 54% | — |
Agentic & Tool Use Qwen3.8 27B leads
o3-mini: 29.6 (#84), Qwen3.8 27B: 32.9 (#57)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
| Cybench | 22.5% | — |
Reasoning Qwen3.8 27B leads
o3-mini: 16.3 (#305), Qwen3.8 27B: 41.0 (#54)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 3% | 42.4% |
| ARC-AGI-1 | 34.5% | 87.5% |
| CritPt | 0.3% | 5.4% |
| LMArena Hard Prompts | 1366 | 1460 |
| DTBench | 68.8% | 88% |
| LMCA | 19% | 41.4% |
| Epoch Capabilities Index | 140.34 | 149.38 |
| SimpleBench | 22.8% | — |
| NYT Connections (extended) | — | 54.5% |
| Chess Puzzles | 17% | — |
| LiveBench Reasoning | 89.6% | — |
| Mystery Game Puzzles | 7% | — |
| LiveBench Data Analysis | 70.6% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 59.6 | — |
| LiveBench | 75.9% | — |
Math Qwen3.8 27B leads
o3-mini: 28.1 (#244), Qwen3.8 27B: 37.1 (#161)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1396 | 1456 |
| FrontierMath (Tiers 1-3) | 18.6% | — |
| FrontierMath Tier 4 | 0% | — |
| OTIS Mock AIME 2024-2025 | 76.9% | — |
| ProofBench | — | 16% |
| LiveBench Math | 77.3% | — |
| MATH Level 5 | 96.5% | — |
| FrontierMath (Feb 2025 set) | 12.4% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Qwen3.8 27B leads
o3-mini: 38.3 (#146), Qwen3.8 27B: 41.6 (#109)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1364 | 1482 |
| GPQA Diamond | 77% | — |
| SimpleQA Verified | 15.3% | — |
| Confabulations | 17.9% | — |
Multimodal Not comparable
o3-mini: —, Qwen3.8 27B: 41.3 (#37)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1271 |
Multilingual Qwen3.8 27B leads
o3-mini: 45.7 (#164), Qwen3.8 27B: 53.7 (#60)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1319 | 1430 |
| LMArena Chinese | 1379 | 1504 |
| LMArena French | 1334 | 1465 |
| LMArena German | 1303 | 1438 |
| LMArena Japanese | 1286 | 1384 |
| LMArena Korean | 1314 | 1393 |
| LMArena Russian | 1304 | 1415 |
| LMArena Spanish | 1321 | 1448 |
Instruction Following Too close to call
o3-mini: 75.1 (#72), Qwen3.8 27B: 75.8 (#53)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1337 | 1439 |
| LiveBench Instruction Following | 84.4% | — |
Long Context Qwen3.8 27B leads
o3-mini: 33.8 (#256), Qwen3.8 27B: 44.3 (#70)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1343 | 1450 |
| Fiction.LiveBench | 50% | — |
Writing & Preference Qwen3.8 27B leads
o3-mini: 50.3 (#182), Qwen3.8 27B: 65.8 (#43)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1337 | 1441 |
| LMArena Creative Writing | 1286 | 1384 |
| LMArena Multi-Turn | 1320 | 1441 |
| Short-Story Creative Writing | 61.7% | — |
| EQ-Bench Creative Writing | — | 1671 |
| LiveBench Language | 50.7% | — |
Frequently asked questions
Is o3-mini better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 36.7 on the Noometry Index.
Which is cheaper, o3-mini or Qwen3.8 27B?
Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is o3-mini or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 40.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 200K.
How many benchmarks do o3-mini and Qwen3.8 27B share?
24 benchmarks have published results for both models. o3-mini has 51 scored results on Noometry and Qwen3.8 27B has 31.