Model comparison
o4-mini vs Qwen3 14B
o4-mini is the stronger model overall, scoring 41.6 to 35.5 on the Noometry Index. Qwen3 14B costs 3.1× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. o4-mini scores higher in 6 categories and Qwen3 14B in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where o4-mini leads 45.5 to 38.1.
- The biggest single-benchmark swing is Chess Puzzles: 26% for o4-mini and 4% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3 14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 35.5 |
| Released | 2025-04-16 | 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 | 60 | 12 |
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Category by category
Coding o4-mini leads
o4-mini: 40.9 (#127), Qwen3 14B: 37.3 (#195)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| SciCode | — | 31.6% |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| LMArena Coding | 1368 | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use o4-mini leads
o4-mini: 32.6 (#61), Qwen3 14B: 29.6 (#83)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | 41% |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning o4-mini leads
o4-mini: 24.6 (#162), Qwen3 14B: 18.5 (#280)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 49.1% |
| CritPt | 0.6% | 0% |
| Chess Puzzles | 26% | 4% |
| DTBench | 77.6% | 64% |
| LMCA | 26.5% | 18.2% |
| Epoch Capabilities Index | 145.64 | 138.23 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| ARC-AGI-1 | 58.7% | — |
| EnigmaEval | 9.2% | — |
| LMArena Hard Prompts | 1351 | — |
| Mystery Game Puzzles | 5% | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.8 (#89), Qwen3 14B: 38.6 (#133)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.7% | 66.4% |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| Omni-MATH | 72% | — |
| LMArena Math | 1389 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge o4-mini leads
o4-mini: 43.6 (#91), Qwen3 14B: 39.3 (#134)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 79.6% | 63.8% |
| Vectara Hallucination Rate | 18.6% | 5.4% |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| GPQA (HELM) | 73.5% | — |
| LMArena Expert | 1343 | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3 14B: —
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Not comparable
o4-mini: 47.0 (#154), Qwen3 14B: —
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1337 | — |
| LMArena Chinese | 1354 | — |
| LMArena French | 1364 | — |
| LMArena German | 1336 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1312 | — |
| LMArena Russian | 1334 | — |
| LMArena Spanish | 1347 | — |
Instruction Following Not comparable
o4-mini: 75.2 (#68), Qwen3 14B: —
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| IFEval | 92.8% | — |
| LMArena Instruction Following | 1321 | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3 14B: 38.1 (#204)
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | 77.8% | 62.5% |
| LMArena Longer Query | 1315 | — |
Writing & Preference Not comparable
o4-mini: 54.0 (#152), Qwen3 14B: —
| Benchmark | o4-mini | Qwen3 14B |
|---|---|---|
| LMArena Text | 1353 | — |
| LMArena Creative Writing | 1294 | — |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
| LMArena Multi-Turn | 1350 | — |
Frequently asked questions
Is o4-mini better than Qwen3 14B?
o4-mini is the stronger model overall, scoring 41.6 to 35.5 on the Noometry Index. Qwen3 14B costs 3.1× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, o4-mini or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3 14B better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 131K.
How many benchmarks do o4-mini and Qwen3 14B share?
11 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3 14B has 12.