Model comparison
o3-pro vs Qwen3-30B-A3B
o3-pro is the stronger model overall, scoring 42.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 163× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Alibaba (Qwen)Summary
- They share 8 benchmarks with published results for both. o3-pro scores higher in 4 categories and Qwen3-30B-A3B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 31.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 97.2% for o3-pro and 40.6% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.9 | 38.9 |
| Released | 2025-06-10 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 200K | 41K |
| Max output | 100K | 16K |
| Input $ / M tokens | $20 | $0.12 |
| Output $ / M tokens | $80 | $0.50 |
| Results tracked | 12 | 32 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| WeirdML | 58.2% | 29.8% |
| Aider Polyglot | 84.9% | — |
| SciCode | — | 33.3% |
| LMArena Coding | — | 1416 |
Agentic & Tool Use Not comparable
o3-pro: —, Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
Reasoning o3-pro leads
o3-pro: 23.8 (#171), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 72.1% | 54.9% |
| DTBench | 86.9% | 69.3% |
| LMCA | 38.5% | 22.4% |
| Epoch Capabilities Index | 147.42 | 139.63 |
| ARC-AGI-2 | 4.9% | — |
| ARC-AGI-1 | 59.3% | — |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 8% |
| LMArena Hard Prompts | — | 1398 |
Math Not comparable
o3-pro: —, Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 47.8% |
| OTIS Mock AIME 2024-2025 | — | 70.3% |
| LMArena Math | — | 1394 |
Knowledge Qwen3-30B-A3B leads
o3-pro: 29.5 (#238), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| Confabulations | 14.2% | 12.3% |
| GPQA Diamond | — | 70.1% |
| Vectara Hallucination Rate | 23.3% | — |
| LMArena Expert | — | 1396 |
Multilingual Not comparable
o3-pro: —, Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | — | 1372 |
| LMArena Chinese | — | 1433 |
| LMArena French | — | 1418 |
| LMArena German | — | 1380 |
| LMArena Japanese | — | 1337 |
| LMArena Korean | — | 1331 |
| LMArena Russian | — | 1370 |
| LMArena Spanish | — | 1404 |
Instruction Following Not comparable
o3-pro: —, Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | — | 1363 |
Long Context o3-pro leads
o3-pro: 72.2 (#1), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 97.2% | 40.6% |
| LMArena Longer Query | — | 1379 |
Writing & Preference o3-pro leads
o3-pro: 57.1 (#133), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | o3-pro | Qwen3-30B-A3B |
|---|---|---|
| Short-Story Creative Writing | 84.4% | 75.3% |
| LMArena Text | — | 1384 |
| LMArena Creative Writing | — | 1317 |
| LMArena Multi-Turn | — | 1378 |
Frequently asked questions
Is o3-pro better than Qwen3-30B-A3B?
o3-pro is the stronger model overall, scoring 42.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 163× 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 Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; o3-pro lists at $20 and $80.
Is o3-pro or Qwen3-30B-A3B better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 41K.
How many benchmarks do o3-pro and Qwen3-30B-A3B share?
8 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen3-30B-A3B has 32.