Model comparison
o3-pro vs Qwen3 14B
o3-pro is the stronger model overall, scoring 42.9 to 35.5 on the Noometry Index. Qwen3 14B costs 57× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. o3-pro scores higher in 3 categories and Qwen3 14B in 1 category; 4 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 38.1.
- The biggest single-benchmark swing is Fiction.LiveBench: 97.2% for o3-pro and 62.5% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen3 14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.9 | 35.5 |
| Released | 2025-06-10 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $20 | $0.35 |
| Output $ / M tokens | $80 | $1.40 |
| Results tracked | 12 | 12 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Qwen3 14B: 37.3 (#195)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| SciCode | — | 31.6% |
| WeirdML | 58.2% | — |
Agentic & Tool Use Not comparable
o3-pro: —, Qwen3 14B: 29.6 (#83)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning o3-pro leads
o3-pro: 23.8 (#171), Qwen3 14B: 18.5 (#280)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 72.1% | 49.1% |
| DTBench | 86.9% | 64% |
| LMCA | 38.5% | 18.2% |
| Epoch Capabilities Index | 147.42 | 138.23 |
| ARC-AGI-2 | 4.9% | — |
| ARC-AGI-1 | 59.3% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
Math Not comparable
o3-pro: —, Qwen3 14B: 38.6 (#133)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
Knowledge Qwen3 14B leads
o3-pro: 29.5 (#238), Qwen3 14B: 39.3 (#134)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Vectara Hallucination Rate | 23.3% | 5.4% |
| GPQA Diamond | — | 63.8% |
| Confabulations | 14.2% | — |
Long Context o3-pro leads
o3-pro: 72.2 (#1), Qwen3 14B: 38.1 (#204)
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | 97.2% | 62.5% |
Writing & Preference Not comparable
o3-pro: 57.1 (#133), Qwen3 14B: —
| Benchmark | o3-pro | Qwen3 14B |
|---|---|---|
| Short-Story Creative Writing | 84.4% | — |
Frequently asked questions
Is o3-pro better than Qwen3 14B?
o3-pro is the stronger model overall, scoring 42.9 to 35.5 on the Noometry Index. Qwen3 14B costs 57× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, o3-pro or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; o3-pro lists at $20 and $80.
Is o3-pro or Qwen3 14B better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 131K.
How many benchmarks do o3-pro and Qwen3 14B share?
6 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen3 14B has 12.