# Qwen2.5-VL 72B Instruct vs Trinity Large Thinking

> Trinity Large Thinking is the stronger model overall, scoring 38.6 to 29.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/qwen2-5-vl-72b-instruct-vs-trinity-large-thinking
- Last updated: 2026-10-12
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where Qwen2.5-VL 72B Instruct leads 20.7 to 16.9.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- Trinity Large Thinking accepts more context: 262K tokens versus 131K.

## Snapshot

| | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| Provider | Alibaba (Qwen) | Arcee AI |
| Noometry Index | 29.9 | 38.6 |
| Rank | 302 | 185 |
| Context | 131K | 262K |
| Input $/M | $2.80 | $0.25 |
| Output $/M | $8.40 | $0.80 |
| Weights | Open | Open |

## Coding

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 34.1 (#244)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
| LMArena Coding | — | 1381 |

## Agentic & Tool Use

- Qwen2.5-VL 72B Instruct: 18.6 (#144)
- Trinity Large Thinking: —

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| OSWorld | 5% | — |

## Reasoning

- Qwen2.5-VL 72B Instruct: 20.7 (#233)
- Trinity Large Thinking: 16.9 (#298)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| Kagi LLM Benchmark | 36% | — |
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 41.6% |
| LMArena Hard Prompts | — | 1350 |
| Surface Evolver Bench | — | 15.6% |

## Math

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 37.6 (#149)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Math | — | 1366 |

## Knowledge

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 40.9 (#113)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | — | 6.9% |
| LMArena Expert | — | 1360 |

## Multimodal

- Qwen2.5-VL 72B Instruct: 33.5 (#97)
- Trinity Large Thinking: —

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

## Multilingual

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 46.2 (#160)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | — | 1325 |
| LMArena Chinese | — | 1373 |
| LMArena French | — | 1374 |
| LMArena German | — | 1356 |
| LMArena Japanese | — | 1311 |
| LMArena Korean | — | 1306 |
| LMArena Russian | — | 1337 |
| LMArena Spanish | — | 1357 |

## Instruction Following

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 70.5 (#162)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | — | 1334 |

## Long Context

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 41.3 (#144)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | — | 1355 |

## Writing & Preference

- Qwen2.5-VL 72B Instruct: —
- Trinity Large Thinking: 53.8 (#158)

| Benchmark | Qwen2.5-VL 72B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1320 |
| LMArena Multi-Turn | — | 1342 |

## FAQ

### Is Qwen2.5-VL 72B Instruct better than Trinity Large Thinking?

Trinity Large Thinking is the stronger model overall, scoring 38.6 to 29.9 on the Noometry Index.

### Which is cheaper, Qwen2.5-VL 72B Instruct or Trinity Large Thinking?

Trinity Large Thinking is cheaper. It lists at $0.25 per million input tokens and $0.80 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.

### Which has the bigger context window?

Trinity Large Thinking does, with 262K tokens against 131K.

### How many benchmarks do Qwen2.5-VL 72B Instruct and Trinity Large Thinking share?

0 benchmarks have published results for both models. Qwen2.5-VL 72B Instruct has 6 scored results on Noometry and Trinity Large Thinking has 24.