Model comparison
o3 vs Qwen3.6 35B-A3B
o3 is the stronger model overall, scoring 47.5 to 37.6 on the Noometry Index. Qwen3.6 35B-A3B costs 6.3× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 10 shared benchmarks.
Alibaba (Qwen)Summary
- They share 10 benchmarks with published results for both. o3 scores higher in 5 categories and Qwen3.6 35B-A3B in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where o3 leads 34.5 to 22.1.
- The biggest single-benchmark swing is WeirdML: 52.4% for o3 and 34.5% for Qwen3.6 35B-A3B.
- Qwen3.6 35B-A3B is cheaper at $0.25 / $1.49 per million input/output tokens, against $2 / $8 for o3.
- Qwen3.6 35B-A3B accepts more context: 262K tokens versus 200K.
- Qwen3.6 35B-A3B has downloadable open weights; the other is API-only.
Side by side
| o3 | Qwen3.6 35B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 47.5 | 37.6 |
| Released | 2025-04-16 | 2026-04-01 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $2 | $0.25 |
| Output $ / M tokens | $8 | $1.49 |
| Results tracked | 63 | 14 |
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Category by category
Coding o3 leads
o3: 46.8 (#64), Qwen3.6 35B-A3B: 37.2 (#196)
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| WeirdML | 52.4% | 34.5% |
| SWE-bench Verified | 62.3% | — |
| SWE-bench Verified (bash only) | 58.4% | — |
| Aider Polyglot | 81.3% | — |
| SciCode | — | 35.8% |
| GSO | 8.8% | — |
| LMArena Coding | 1408 | — |
| CadEval | 74% | — |
| ALE-Bench | 933.55 | — |
Agentic & Tool Use o3 leads
o3: 34.5 (#44), Qwen3.6 35B-A3B: 22.1 (#134)
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| Terminal-Bench | — | 23% |
| Berkeley Function Calling Leaderboard | 63% | — |
| GDPval | 30.8% | — |
| DeepResearch Bench | 45.2% | — |
| OSWorld | 23% | — |
| LMArena Search | 1144 | — |
| METR Time Horizons | 65.4% | — |
Reasoning o3 leads
o3: 32.0 (#78), Qwen3.6 35B-A3B: 28.0 (#109)
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| CritPt | 1.4% | 0.3% |
| Chess Puzzles | 38% | 26% |
| Mystery Game Puzzles | 29% | 22% |
| DTBench | 84.8% | 73.9% |
| LMCA | 39.7% | 29.7% |
| Epoch Capabilities Index | 146.86 | 143.93 |
| ARC-AGI-2 | 6.5% | — |
| SimpleBench | 53.1% | — |
| Kagi LLM Benchmark | 67.6% | — |
| NYT Connections (extended) | — | 41.6% |
| ARC-AGI-1 | 60.8% | — |
| EnigmaEval | 13.1% | — |
| LMArena Hard Prompts | 1402 | — |
| Surface Evolver Bench | — | 44.4% |
| ForecastBench | 62.5 | — |
Math o3 leads
o3: 50.2 (#58), Qwen3.6 35B-A3B: 38.9 (#121)
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 33.3% | 20.4% |
| OTIS Mock AIME 2024-2025 | 84.4% | 86.7% |
| Omni-MATH | 71.4% | — |
| LMArena Math | 1426 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 18.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge o3 leads
o3: 54.6 (#52), Qwen3.6 35B-A3B: 51.3 (#68)
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| GPQA Diamond | 81.8% | 84.8% |
| Humanity's Last Exam | 20.3% | — |
| SimpleQA Verified | 49.4% | — |
| MMLU-Pro | 85.9% | — |
| Confabulations | 14.4% | — |
| GPQA (HELM) | 75.3% | — |
| LMArena Expert | 1402 | — |
Multimodal Not comparable
o3: 41.4 (#36), Qwen3.6 35B-A3B: —
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Vision | 1214 | — |
| GeoBench | 74% | — |
| VPCT | 52% | — |
Multilingual Not comparable
o3: 51.7 (#105), Qwen3.6 35B-A3B: —
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Non-English | 1401 | — |
| LMArena Chinese | 1437 | — |
| LMArena French | 1430 | — |
| LMArena German | 1420 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1370 | — |
| LMArena Russian | 1406 | — |
| LMArena Spanish | 1395 | — |
Instruction Following Not comparable
o3: 72.8 (#127), Qwen3.6 35B-A3B: —
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| IFEval | 86.9% | — |
| LMArena Instruction Following | 1368 | — |
Long Context Not comparable
o3: 53.3 (#6), Qwen3.6 35B-A3B: —
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| Fiction.LiveBench | 88.9% | — |
| CL-bench | 17.8% | — |
| LMArena Longer Query | 1372 | — |
Writing & Preference Not comparable
o3: 63.5 (#64), Qwen3.6 35B-A3B: —
| Benchmark | o3 | Qwen3.6 35B-A3B |
|---|---|---|
| LMArena Text | 1410 | — |
| LMArena Creative Writing | 1359 | — |
| Short-Story Creative Writing | 83.9% | — |
| EQ-Bench Creative Writing | 1676 | — |
| WildBench | 86.1% | — |
| LMArena Multi-Turn | 1405 | — |
Frequently asked questions
Is o3 better than Qwen3.6 35B-A3B?
o3 is the stronger model overall, scoring 47.5 to 37.6 on the Noometry Index. Qwen3.6 35B-A3B costs 6.3× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, o3 or Qwen3.6 35B-A3B?
Qwen3.6 35B-A3B is cheaper. It lists at $0.25 per million input tokens and $1.49 per million output tokens; o3 lists at $2 and $8.
Is o3 or Qwen3.6 35B-A3B better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 37.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 35B-A3B does, with 262K tokens against 200K.
How many benchmarks do o3 and Qwen3.6 35B-A3B share?
10 benchmarks have published results for both models. o3 has 63 scored results on Noometry and Qwen3.6 35B-A3B has 14.