Model comparison
o1 vs Qwen2.5-VL 72B Instruct
o1 is the stronger model overall, scoring 40.9 to 29.9 on the Noometry Index. Qwen2.5-VL 72B Instruct costs 6.3× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. o1 scores higher in 3 categories and Qwen2.5-VL 72B Instruct in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where o1 leads 27.9 to 20.7.
- The biggest single-benchmark swing is GeoBench: 80% for o1 and 62% for Qwen2.5-VL 72B Instruct.
- Qwen2.5-VL 72B Instruct is cheaper at $2.80 / $8.40 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 131K.
- Qwen2.5-VL 72B Instruct has downloadable open weights; the other is API-only.
Side by side
| o1 | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 40.9 | 29.9 |
| Released | 2024-09-12 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $15 | $2.80 |
| Output $ / M tokens | $60 | $8.40 |
| Results tracked | 52 | 6 |
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Category by category
Coding Not comparable
o1: 46.1 (#70), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Aider Polyglot | 61.7% | — |
| WeirdML | 47.6% | — |
| LiveBench Coding | 69.7% | — |
| LMArena Coding | 1367 | — |
| CadEval | 56% | — |
| HumanEval+ | 89% | — |
| MBPP+ | 80.2% | — |
Agentic & Tool Use o1 leads
o1: 24.6 (#117), Qwen2.5-VL 72B Instruct: 18.6 (#144)
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Cybench | 10% | — |
| OSWorld | — | 5% |
| METR Time Horizons | 51.1% | — |
Reasoning o1 leads
o1: 27.9 (#111), Qwen2.5-VL 72B Instruct: 20.7 (#233)
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| SimpleBench | 41.7% | — |
| Kagi LLM Benchmark | — | 36% |
| ARC-AGI-1 | 30.7% | — |
| Chess Puzzles | 15% | — |
| EnigmaEval | 5.7% | — |
| LiveBench Reasoning | 91.6% | — |
| LMArena Hard Prompts | 1371 | — |
| DTBench | 74.7% | — |
| LiveBench Data Analysis | 65.5% | — |
| LMCA | 22.3% | — |
| Epoch Capabilities Index | 141.91 | — |
| LiveBench | 75.7% | — |
Math Not comparable
o1: 36.1 (#175), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 14.7% | — |
| OTIS Mock AIME 2024-2025 | 73.3% | — |
| LiveBench Math | 80.3% | — |
| LMArena Math | 1388 | — |
| MATH Level 5 | 94.7% | — |
| FrontierMath (Feb 2025 set) | 9.3% | — |
Knowledge Not comparable
o1: 41.5 (#110), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 76.8% | — |
| Humanity's Last Exam | 8% | — |
| SimpleQA Verified | 41.1% | — |
| Confabulations | 11.7% | — |
| LMArena Expert | 1361 | — |
Multimodal Too close to call
o1: 34.2 (#93), Qwen2.5-VL 72B Instruct: 33.5 (#97)
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | 1168 | 1107 |
| GeoBench | 80% | 62% |
| SpatialViz-Bench | 41.4% | 33.3% |
| Video-MME | — | 73.5% |
| VPCT | 37% | — |
Multilingual Not comparable
o1: 48.6 (#142), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1358 | — |
| LMArena Chinese | 1394 | — |
| LMArena French | 1344 | — |
| LMArena German | 1337 | — |
| LMArena Japanese | 1346 | — |
| LMArena Korean | 1396 | — |
| LMArena Russian | 1356 | — |
| LMArena Spanish | 1345 | — |
Instruction Following Not comparable
o1: 74.8 (#86), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| LMArena Instruction Following | 1367 | — |
Long Context Not comparable
o1: 50.3 (#9), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| LMArena Longer Query | 1378 | — |
Writing & Preference Not comparable
o1: 55.6 (#144), Qwen2.5-VL 72B Instruct: —
| Benchmark | o1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1366 | — |
| LMArena Creative Writing | 1348 | — |
| Short-Story Creative Writing | 70.2% | — |
| LMArena Multi-Turn | 1369 | — |
| LiveBench Language | 65.4% | — |
Frequently asked questions
Is o1 better than Qwen2.5-VL 72B Instruct?
o1 is the stronger model overall, scoring 40.9 to 29.9 on the Noometry Index. Qwen2.5-VL 72B Instruct costs 6.3× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, o1 or Qwen2.5-VL 72B Instruct?
Qwen2.5-VL 72B Instruct is cheaper. It lists at $2.80 per million input tokens and $8.40 per million output tokens; o1 lists at $15 and $60.
Which has the bigger context window?
o1 does, with 200K tokens against 131K.
How many benchmarks do o1 and Qwen2.5-VL 72B Instruct share?
3 benchmarks have published results for both models. o1 has 52 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.