Model comparison
o1 vs Qwen3.5 122B-A10B
Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 to 40.9 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. o1 scores higher in 5 categories and Qwen3.5 122B-A10B in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 43.0.
- The biggest single-benchmark swing is LMCA: 22.3% for o1 and 32.2% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $15 / $60 for o1.
- Qwen3.5 122B-A10B accepts more context: 262K tokens versus 200K.
- Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.
Side by side
| o1 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 40.9 | 42.1 |
| Released | 2024-09-12 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $15 | $0.40 |
| Output $ / M tokens | $60 | $3.20 |
| Results tracked | 52 | 27 |
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Category by category
Coding o1 leads
o1: 46.1 (#70), Qwen3.5 122B-A10B: 39.1 (#162)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Coding | 1367 | 1436 |
| Aider Polyglot | 61.7% | — |
| LMArena WebDev | — | 1360 |
| SciCode | — | 35.6% |
| WeirdML | 47.6% | — |
| LiveBench Coding | 69.7% | — |
| CadEval | 56% | — |
| HumanEval+ | 89% | — |
| MBPP+ | 80.2% | — |
Agentic & Tool Use Not comparable
o1: 24.6 (#117), Qwen3.5 122B-A10B: —
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| Cybench | 10% | — |
| METR Time Horizons | 51.1% | — |
Reasoning Too close to call
o1: 27.9 (#111), Qwen3.5 122B-A10B: 27.2 (#123)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Hard Prompts | 1371 | 1421 |
| DTBench | 74.7% | 84.3% |
| LMCA | 22.3% | 32.2% |
| SimpleBench | 41.7% | — |
| NYT Connections (extended) | — | 51.7% |
| ARC-AGI-1 | 30.7% | — |
| CritPt | — | 0.9% |
| Chess Puzzles | 15% | — |
| EnigmaEval | 5.7% | — |
| Thematic Generalization | — | 51.2% |
| LiveBench Reasoning | 91.6% | — |
| Mystery Game Puzzles | — | 17% |
| LiveBench Data Analysis | 65.5% | — |
| Epoch Capabilities Index | 141.91 | — |
| LiveBench | 75.7% | — |
Math Qwen3.5 122B-A10B leads
o1: 36.1 (#175), Qwen3.5 122B-A10B: 39.1 (#112)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1388 | 1432 |
| FrontierMath (Tiers 1-3) | 14.7% | — |
| OTIS Mock AIME 2024-2025 | 73.3% | — |
| LiveBench Math | 80.3% | — |
| MATH Level 5 | 94.7% | — |
| FrontierMath (Feb 2025 set) | 9.3% | — |
Knowledge o1 leads
o1: 41.5 (#110), Qwen3.5 122B-A10B: 38.8 (#142)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Expert | 1361 | 1432 |
| GPQA Diamond | 76.8% | — |
| Humanity's Last Exam | 8% | — |
| SimpleQA Verified | 41.1% | — |
| Confabulations | 11.7% | — |
| Vectara Hallucination Rate | — | 11.2% |
Multimodal Qwen3.5 122B-A10B leads
o1: 34.2 (#93), Qwen3.5 122B-A10B: 39.6 (#57)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | 1168 | 1245 |
| GeoBench | 80% | — |
| VPCT | 37% | — |
| SpatialViz-Bench | 41.4% | — |
Multilingual Qwen3.5 122B-A10B leads
o1: 48.6 (#142), Qwen3.5 122B-A10B: 51.6 (#107)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1358 | 1400 |
| LMArena Chinese | 1394 | 1462 |
| LMArena French | 1344 | 1442 |
| LMArena German | 1337 | 1426 |
| LMArena Japanese | 1346 | 1367 |
| LMArena Korean | 1396 | 1352 |
| LMArena Russian | 1356 | 1400 |
| LMArena Spanish | 1345 | 1424 |
Instruction Following Too close to call
o1: 74.8 (#86), Qwen3.5 122B-A10B: 73.8 (#115)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1399 |
| LiveBench Instruction Following | 81.5% | — |
Long Context o1 leads
o1: 50.3 (#9), Qwen3.5 122B-A10B: 43.0 (#109)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1378 | 1410 |
| Fiction.LiveBench | 83.3% | — |
Writing & Preference Qwen3.5 122B-A10B leads
o1: 55.6 (#144), Qwen3.5 122B-A10B: 60.0 (#105)
| Benchmark | o1 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1366 | 1417 |
| LMArena Creative Writing | 1348 | 1368 |
| LMArena Multi-Turn | 1369 | 1416 |
| Short-Story Creative Writing | 70.2% | — |
| LiveBench Language | 65.4% | — |
Frequently asked questions
Is o1 better than Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 to 40.9 on the Noometry Index.
Which is cheaper, o1 or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; o1 lists at $15 and $60.
Is o1 or Qwen3.5 122B-A10B better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 122B-A10B does, with 262K tokens against 200K.
How many benchmarks do o1 and Qwen3.5 122B-A10B share?
20 benchmarks have published results for both models. o1 has 52 scored results on Noometry and Qwen3.5 122B-A10B has 27.