# o4-mini vs Qwen2.5-VL 72B Instruct

> o4-mini is the stronger model overall, scoring 41.6 to 29.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/o4-mini-vs-qwen2-5-vl-72b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. o4-mini scores higher in 3 categories and Qwen2.5-VL 72B Instruct in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where o4-mini leads 32.3 to 18.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 67.6% for o4-mini and 36% for Qwen2.5-VL 72B Instruct.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- o4-mini accepts more context: 200K tokens versus 131K.
- Qwen2.5-VL 72B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 29.4 |
| Rank | 119 | 298 |
| Context | 200K | 131K |
| Input $/M | $1.10 | $2.80 |
| Output $/M | $4.40 | $8.40 |
| Weights | Proprietary | Open |

## Coding

- o4-mini: 41.6 (#110)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| LMArena Coding | 1369 | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |

## Agentic & Tool Use

- o4-mini: 32.3 (#57)
- Qwen2.5-VL 72B Instruct: 18.4 (#136)

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| OSWorld | — | 5% |
| METR Time Horizons | 63.9% | — |

## Reasoning

- o4-mini: 24.6 (#143)
- Qwen2.5-VL 72B Instruct: 20.0 (#228)

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 36% |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| ARC-AGI-1 | 58.7% | — |
| CritPt | 0.6% | — |
| Chess Puzzles | 26% | — |
| EnigmaEval | 9.2% | — |
| LMArena Hard Prompts | 1351 | — |
| Mystery Game Puzzles | 5% | — |
| DTBench | 77.6% | — |
| LMCA | 26.5% | — |
| Epoch Capabilities Index | 145.63 | — |
| ForecastBench | 61.8 | — |

## Math

- o4-mini: 40.2 (#79)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 81.7% | — |
| Omni-MATH | 72% | — |
| LMArena Math | 1390 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |

## Knowledge

- o4-mini: 43.5 (#83)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 79.6% | — |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
| LMArena Expert | 1344 | — |

## Multimodal

- o4-mini: 39.5 (#46)
- Qwen2.5-VL 72B Instruct: 32.9 (#92)

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | 1194 | 1107 |
| GeoBench | 64% | 62% |
| Video-MME | — | 73.5% |
| VPCT | 57.5% | — |
| SpatialViz-Bench | — | 33.3% |

## Multilingual

- o4-mini: 47.2 (#149)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1336 | — |
| LMArena Chinese | 1355 | — |
| LMArena French | 1362 | — |
| LMArena German | 1336 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1313 | — |
| LMArena Russian | 1334 | — |
| LMArena Spanish | 1346 | — |

## Instruction Following

- o4-mini: 75.4 (#63)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| IFEval | 92.8% | — |
| LMArena Instruction Following | 1321 | — |

## Long Context

- o4-mini: 45.5 (#31)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Fiction.LiveBench | 77.8% | — |
| LMArena Longer Query | 1315 | — |

## Writing & Preference

- o4-mini: 54.2 (#148)
- Qwen2.5-VL 72B Instruct: —

| Benchmark | o4-mini | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1353 | — |
| LMArena Creative Writing | 1294 | — |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
| LMArena Multi-Turn | 1350 | — |

## FAQ

### Is o4-mini better than Qwen2.5-VL 72B Instruct?

o4-mini is the stronger model overall, scoring 41.6 to 29.4 on the Noometry Index.

### Which is cheaper, o4-mini or Qwen2.5-VL 72B Instruct?

o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.

### Which has the bigger context window?

o4-mini does, with 200K tokens against 131K.

### How many benchmarks do o4-mini and Qwen2.5-VL 72B Instruct share?

3 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.