Model comparison
o3-mini vs Qwen3 14B
o3-mini is the stronger model overall, scoring 36.7 to 35.0 on the Noometry Index. Qwen3 14B costs 3.1× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 9 shared benchmarks.
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. o3-mini scores higher in 1 category and Qwen3 14B in 5 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 14B leads 37.0 to 27.6.
- The biggest single-benchmark swing is GPQA Diamond: 77% for o3-mini and 63.8% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| o3-mini | Qwen3 14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 36.7 | 35.0 |
| Released | 2024-12-20 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $1.10 | $0.35 |
| Output $ / M tokens | $4.40 | $1.40 |
| Results tracked | 51 | 12 |
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Category by category
Coding o3-mini leads
o3-mini: 41.4 (#116), Qwen3 14B: 36.7 (#191)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| SciCode | 39.8% | 31.6% |
| Aider Polyglot | 60.4% | — |
| GSO | 1.3% | — |
| WeirdML | 43.7% | — |
| LiveBench Coding | 82.7% | — |
| LMArena Coding | 1378 | — |
| CadEval | 54% | — |
Agentic & Tool Use Too close to call
o3-mini: 29.3 (#79), Qwen3 14B: 29.4 (#78)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
| Cybench | 22.5% | — |
Reasoning Qwen3 14B leads
o3-mini: 16.3 (#295), Qwen3 14B: 18.1 (#272)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| CritPt | 0.3% | 0% |
| Chess Puzzles | 17% | 4% |
| DTBench | 68.8% | 64% |
| LMCA | 19% | 18.2% |
| Epoch Capabilities Index | 140.35 | 138.23 |
| ARC-AGI-2 | 3% | — |
| SimpleBench | 22.8% | — |
| Kagi LLM Benchmark | — | 49.1% |
| ARC-AGI-1 | 34.5% | — |
| LiveBench Reasoning | 89.6% | — |
| LMArena Hard Prompts | 1366 | — |
| Mystery Game Puzzles | 7% | — |
| LiveBench Data Analysis | 70.6% | — |
| ForecastBench | 59.6 | — |
| LiveBench | 75.9% | — |
Math Qwen3 14B leads
o3-mini: 27.6 (#236), Qwen3 14B: 37.0 (#102)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 76.9% | 66.4% |
| FrontierMath (Tiers 1-3) | 18.6% | — |
| FrontierMath Tier 4 | 0% | — |
| LiveBench Math | 77.3% | — |
| LMArena Math | 1396 | — |
| MATH Level 5 | 96.5% | — |
| FrontierMath (Feb 2025 set) | 12.4% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Too close to call
o3-mini: 38.3 (#135), Qwen3 14B: 39.2 (#124)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 77% | 63.8% |
| SimpleQA Verified | 15.3% | — |
| Confabulations | 17.9% | — |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1364 | — |
Multilingual Not comparable
o3-mini: 45.9 (#159), Qwen3 14B: —
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1319 | — |
| LMArena Chinese | 1379 | — |
| LMArena French | 1333 | — |
| LMArena German | 1303 | — |
| LMArena Japanese | 1286 | — |
| LMArena Korean | 1314 | — |
| LMArena Russian | 1304 | — |
| LMArena Spanish | 1321 | — |
Instruction Following Not comparable
o3-mini: 75.3 (#67), Qwen3 14B: —
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| LiveBench Instruction Following | 84.4% | — |
| LMArena Instruction Following | 1338 | — |
Long Context Qwen3 14B leads
o3-mini: 33.8 (#251), Qwen3 14B: 38.0 (#201)
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | 50% | 62.5% |
| LMArena Longer Query | 1343 | — |
Writing & Preference Not comparable
o3-mini: 50.4 (#177), Qwen3 14B: —
| Benchmark | o3-mini | Qwen3 14B |
|---|---|---|
| LMArena Text | 1337 | — |
| LMArena Creative Writing | 1286 | — |
| Short-Story Creative Writing | 61.7% | — |
| LMArena Multi-Turn | 1320 | — |
| LiveBench Language | 50.7% | — |
Frequently asked questions
Is o3-mini better than Qwen3 14B?
o3-mini is the stronger model overall, scoring 36.7 to 35.0 on the Noometry Index. Qwen3 14B costs 3.1× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, o3-mini or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is o3-mini or Qwen3 14B better for coding?
o3-mini scores higher on coding benchmarks: 41.4 versus 36.7 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 131K.
How many benchmarks do o3-mini and Qwen3 14B share?
9 benchmarks have published results for both models. o3-mini has 51 scored results on Noometry and Qwen3 14B has 12.