Model comparison
o1-pro vs Qwen3-Next 80B-A3B Instruct
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 42.5 to 31.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where Qwen3-Next 80B-A3B Instruct leads 41.3 to 29.3.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 131K.
- Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.
Side by side
| o1-pro | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 31.2 | 42.5 |
| Released | 2025-03-19 | 2025-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 33K |
| Input $ / M tokens | $150 | $0.50 |
| Output $ / M tokens | $600 | $2 |
| Results tracked | 3 | 25 |
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Category by category
Coding Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 41.9 (#101)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | — | 1441 |
Reasoning Qwen3-Next 80B-A3B Instruct leads
o1-pro: 20.2 (#223), Qwen3-Next 80B-A3B Instruct: 30.1 (#79)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | — | 66.7% |
| ARC-AGI-1 | 23.3% | — |
| EnigmaEval | 6.1% | — |
| LMArena Hard Prompts | — | 1428 |
Math Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 36.3 (#118)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Omni-MATH | — | 46.7% |
| LMArena Math | — | 1440 |
Knowledge Qwen3-Next 80B-A3B Instruct leads
o1-pro: 29.3 (#227), Qwen3-Next 80B-A3B Instruct: 41.3 (#102)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Humanity's Last Exam | 8.1% | — |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
| LMArena Expert | — | 1416 |
Multilingual Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 52.3 (#88)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | — | 1407 |
| LMArena Chinese | — | 1461 |
| LMArena French | — | 1415 |
| LMArena German | — | 1416 |
| LMArena Japanese | — | 1398 |
| LMArena Korean | — | 1366 |
| LMArena Russian | — | 1403 |
| LMArena Spanish | — | 1436 |
Instruction Following Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 71.0 (#154)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| IFEval | — | 81% |
| LMArena Instruction Following | — | 1388 |
Long Context Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 37.0 (#218)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Fiction.LiveBench | — | 55.6% |
| LMArena Longer Query | — | 1403 |
Writing & Preference Not comparable
o1-pro: —, Qwen3-Next 80B-A3B Instruct: 58.2 (#116)
| Benchmark | o1-pro | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | — | 1417 |
| LMArena Creative Writing | — | 1333 |
| WildBench | — | 80.7% |
| LMArena Multi-Turn | — | 1415 |
Frequently asked questions
Is o1-pro better than Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 42.5 to 31.2 on the Noometry Index.
Which is cheaper, o1-pro or Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper. It lists at $0.50 per million input tokens and $2 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
o1-pro does, with 200K tokens against 131K.
How many benchmarks do o1-pro and Qwen3-Next 80B-A3B Instruct share?
0 benchmarks have published results for both models. o1-pro has 3 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.