Model comparison
o1 vs Qwen3-30B-A3B
o1 is the stronger model overall, scoring 40.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 122× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. o1 scores higher in 4 categories and Qwen3-30B-A3B in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 31.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 83.3% for o1 and 40.6% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| o1 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 40.9 | 38.9 |
| Released | 2024-09-12 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 200K | 41K |
| Max output | 100K | 16K |
| Input $ / M tokens | $15 | $0.12 |
| Output $ / M tokens | $60 | $0.50 |
| Results tracked | 52 | 32 |
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Category by category
Coding o1 leads
o1: 46.1 (#70), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| WeirdML | 47.6% | 29.8% |
| LMArena Coding | 1367 | 1416 |
| Aider Polyglot | 61.7% | — |
| SciCode | — | 33.3% |
| LiveBench Coding | 69.7% | — |
| CadEval | 56% | — |
| HumanEval+ | 89% | — |
| MBPP+ | 80.2% | — |
Agentic & Tool Use Qwen3-30B-A3B leads
o1: 24.6 (#117), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
| Cybench | 10% | — |
| METR Time Horizons | 51.1% | — |
Reasoning o1 leads
o1: 27.9 (#111), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| Chess Puzzles | 15% | 8% |
| LMArena Hard Prompts | 1371 | 1398 |
| DTBench | 74.7% | 69.3% |
| LMCA | 22.3% | 22.4% |
| Epoch Capabilities Index | 141.91 | 139.63 |
| SimpleBench | 41.7% | — |
| Kagi LLM Benchmark | — | 54.9% |
| ARC-AGI-1 | 30.7% | — |
| CritPt | — | 0.3% |
| EnigmaEval | 5.7% | — |
| LiveBench Reasoning | 91.6% | — |
| LiveBench Data Analysis | 65.5% | — |
| LiveBench | 75.7% | — |
Math Qwen3-30B-A3B leads
o1: 36.1 (#175), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.3% | 70.3% |
| LMArena Math | 1388 | 1394 |
| FrontierMath (Tiers 1-3) | 14.7% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| LiveBench Math | 80.3% | — |
| MATH Level 5 | 94.7% | — |
| FrontierMath (Feb 2025 set) | 9.3% | — |
Knowledge Too close to call
o1: 41.5 (#110), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 76.8% | 70.1% |
| Confabulations | 11.7% | 12.3% |
| LMArena Expert | 1361 | 1396 |
| Humanity's Last Exam | 8% | — |
| SimpleQA Verified | 41.1% | — |
Multimodal Not comparable
o1: 34.2 (#93), Qwen3-30B-A3B: —
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1168 | — |
| GeoBench | 80% | — |
| VPCT | 37% | — |
| SpatialViz-Bench | 41.4% | — |
Multilingual Too close to call
o1: 48.6 (#142), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1358 | 1372 |
| LMArena Chinese | 1394 | 1433 |
| LMArena French | 1344 | 1418 |
| LMArena German | 1337 | 1380 |
| LMArena Japanese | 1346 | 1337 |
| LMArena Korean | 1396 | 1331 |
| LMArena Russian | 1356 | 1370 |
| LMArena Spanish | 1345 | 1404 |
Instruction Following o1 leads
o1: 74.8 (#86), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1363 |
| LiveBench Instruction Following | 81.5% | — |
Long Context o1 leads
o1: 50.3 (#9), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 83.3% | 40.6% |
| LMArena Longer Query | 1378 | 1379 |
Writing & Preference Too close to call
o1: 55.6 (#144), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | o1 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1366 | 1384 |
| LMArena Creative Writing | 1348 | 1317 |
| Short-Story Creative Writing | 70.2% | 75.3% |
| LMArena Multi-Turn | 1369 | 1378 |
| LiveBench Language | 65.4% | — |
Frequently asked questions
Is o1 better than Qwen3-30B-A3B?
o1 is the stronger model overall, scoring 40.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 122× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, o1 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; o1 lists at $15 and $60.
Is o1 or Qwen3-30B-A3B better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
o1 does, with 200K tokens against 41K.
How many benchmarks do o1 and Qwen3-30B-A3B share?
27 benchmarks have published results for both models. o1 has 52 scored results on Noometry and Qwen3-30B-A3B has 32.