Model comparison
o3-mini vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 45.3 to 36.7 on the Noometry Index.
Last verified . 22 shared benchmarks.
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. o3-mini scores higher in 0 categories and Qwen3.8 27B in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.8 27B leads 65.9 to 50.4.
- The biggest single-benchmark swing is LMCA: 19% for o3-mini and 41.4% 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 | 45.3 |
| 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 | 28 |
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Category by category
Coding Qwen3.8 27B leads
o3-mini: 41.4 (#116), Qwen3.8 27B: 50.3 (#42)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| SciCode | 39.8% | 46.6% |
| LMArena Coding | 1378 | 1487 |
| Aider Polyglot | 60.4% | — |
| LMArena WebDev | — | 1592 |
| GSO | 1.3% | — |
| WeirdML | 43.7% | — |
| LiveBench Coding | 82.7% | — |
| CadEval | 54% | — |
Agentic & Tool Use Not comparable
o3-mini: 29.3 (#79), Qwen3.8 27B: —
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| Cybench | 22.5% | — |
Reasoning Qwen3.8 27B leads
o3-mini: 16.3 (#295), Qwen3.8 27B: 31.5 (#71)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| CritPt | 0.3% | 5.4% |
| LMArena Hard Prompts | 1366 | 1459 |
| DTBench | 68.8% | 88% |
| LMCA | 19% | 41.4% |
| Epoch Capabilities Index | 140.35 | 149.38 |
| ARC-AGI-2 | 3% | — |
| SimpleBench | 22.8% | — |
| NYT Connections (extended) | — | 54.5% |
| ARC-AGI-1 | 34.5% | — |
| 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: 27.6 (#236), Qwen3.8 27B: 35.7 (#139)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1396 | 1453 |
| 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 (#135), Qwen3.8 27B: 40.9 (#105)
| 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: 40.5 (#36)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1270 |
Multilingual Qwen3.8 27B leads
o3-mini: 45.9 (#159), Qwen3.8 27B: 54.0 (#54)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1319 | 1431 |
| LMArena Chinese | 1379 | 1504 |
| LMArena French | 1333 | 1468 |
| LMArena German | 1303 | 1435 |
| LMArena Japanese | 1286 | 1392 |
| LMArena Korean | 1314 | 1389 |
| LMArena Russian | 1304 | 1421 |
| LMArena Spanish | 1321 | 1444 |
Instruction Following Too close to call
o3-mini: 75.3 (#67), Qwen3.8 27B: 75.8 (#54)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1338 | 1436 |
| LiveBench Instruction Following | 84.4% | — |
Long Context Qwen3.8 27B leads
o3-mini: 33.8 (#251), Qwen3.8 27B: 44.3 (#66)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1343 | 1448 |
| Fiction.LiveBench | 50% | — |
Writing & Preference Qwen3.8 27B leads
o3-mini: 50.4 (#177), Qwen3.8 27B: 65.9 (#41)
| Benchmark | o3-mini | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1337 | 1441 |
| LMArena Creative Writing | 1286 | 1382 |
| LMArena Multi-Turn | 1320 | 1443 |
| 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 45.3 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: 50.3 versus 41.4 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?
22 benchmarks have published results for both models. o3-mini has 51 scored results on Noometry and Qwen3.8 27B has 28.