# o3-pro vs Qwen2.5-VL 72B Instruct

> o3-pro is the stronger model overall, scoring 42.7 to 29.4 on the Noometry Index. Qwen2.5-VL 72B Instruct costs 8.3× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/o3-pro-vs-qwen2-5-vl-72b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. o3-pro scores higher in 1 category and Qwen2.5-VL 72B Instruct in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in reasoning, where o3-pro leads 24.1 to 20.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 72.1% for o3-pro and 36% for Qwen2.5-VL 72B Instruct.
- Qwen2.5-VL 72B Instruct is cheaper at $2.80 / $8.40 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 131K.
- Qwen2.5-VL 72B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.7 | 29.4 |
| Rank | 94 | 298 |
| Context | 200K | 131K |
| Input $/M | $20 | $2.80 |
| Output $/M | $80 | $8.40 |
| Weights | Proprietary | Open |

## Coding

- o3-pro: 55.1 (#22)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| WeirdML | 58.2% | — |

## Agentic & Tool Use

- o3-pro: —
- Qwen2.5-VL 72B Instruct: 18.4 (#136)

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| OSWorld | — | 5% |

## Reasoning

- o3-pro: 24.1 (#152)
- Qwen2.5-VL 72B Instruct: 20.0 (#228)

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 72.1% | 36% |
| ARC-AGI-2 | 4.9% | — |
| ARC-AGI-1 | 59.3% | — |
| DTBench | 86.9% | — |
| LMCA | 38.5% | — |
| Epoch Capabilities Index | 147.43 | — |

## Knowledge

- o3-pro: 29.0 (#234)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |

## Multimodal

- o3-pro: —
- Qwen2.5-VL 72B Instruct: 32.9 (#92)

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |

## Long Context

- o3-pro: 72.1 (#1)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |

## Writing & Preference

- o3-pro: 57.1 (#128)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o3-pro | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Short-Story Creative Writing | 84.4% | — |

## FAQ

### Is o3-pro better than Qwen2.5-VL 72B Instruct?

o3-pro is the stronger model overall, scoring 42.7 to 29.4 on the Noometry Index. Qwen2.5-VL 72B Instruct costs 8.3× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

### Which is cheaper, o3-pro 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; o3-pro lists at $20 and $80.

### Which has the bigger context window?

o3-pro does, with 200K tokens against 131K.

### How many benchmarks do o3-pro and Qwen2.5-VL 72B Instruct share?

1 benchmark has published results for both models. o3-pro has 12 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.