# Qwen2.5 7B Instruct vs Trinity Large Thinking

> Trinity Large Thinking is the stronger model overall, scoring 38.6 to 29.0 on the Noometry Index.

- Canonical page: https://noometry.com/compare/qwen2-5-7b-instruct-vs-trinity-large-thinking
- Last updated: 2026-10-12
- Shared benchmarks: 0

## Summary

- The widest gap is in math, where Trinity Large Thinking leads 37.6 to 12.6.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.25 / $0.80 for Trinity Large Thinking.
- Trinity Large Thinking accepts more context: 262K tokens versus 131K.

## Snapshot

| | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| Provider | Alibaba (Qwen) | Arcee AI |
| Noometry Index | 29.0 | 38.6 |
| Rank | 320 | 185 |
| Context | 131K | 262K |
| Input $/M | $0.17 | $0.25 |
| Output $/M | $0.70 | $0.80 |
| Weights | Open | Open |

## Coding

- Qwen2.5 7B Instruct: 36.5 (#208)
- Trinity Large Thinking: 34.1 (#244)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
| BigCodeBench Instruct | 37.6% | — |
| LMArena Coding | — | 1381 |
| BigCodeBench Complete | 46.1% | — |

## Agentic & Tool Use

- Qwen2.5 7B Instruct: 23.8 (#124)
- Trinity Large Thinking: —

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| BALROG | 7.8% | — |

## Reasoning

- Qwen2.5 7B Instruct: 14.8 (#322)
- Trinity Large Thinking: 16.9 (#298)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 41.6% |
| LMArena Hard Prompts | — | 1350 |
| DTBench | 47.7% | — |
| LMCA | 6.4% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 118.51 | — |

## Math

- Qwen2.5 7B Instruct: 12.6 (#306)
- Trinity Large Thinking: 37.6 (#149)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.5% | — |
| Omni-MATH | 29.4% | — |
| LMArena Math | — | 1366 |

## Knowledge

- Qwen2.5 7B Instruct: 17.0 (#286)
- Trinity Large Thinking: 40.9 (#113)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| GPQA Diamond | 35.5% | — |
| MMLU-Pro | 53.9% | — |
| Vectara Hallucination Rate | — | 6.9% |
| GPQA (HELM) | 34.1% | — |
| LMArena Expert | — | 1360 |
| MMLU | 72.9% | — |

## Multilingual

- Qwen2.5 7B Instruct: —
- Trinity Large Thinking: 46.2 (#160)

| Benchmark | Qwen2.5 7B 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 7B Instruct: 63.2 (#231)
- Trinity Large Thinking: 70.5 (#162)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| IFEval | 74.1% | — |
| LMArena Instruction Following | — | 1334 |

## Long Context

- Qwen2.5 7B Instruct: —
- Trinity Large Thinking: 41.3 (#144)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | — | 1355 |

## Writing & Preference

- Qwen2.5 7B Instruct: 48.8 (#195)
- Trinity Large Thinking: 53.8 (#158)

| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1320 |
| WildBench | 73.1% | — |
| LMArena Multi-Turn | — | 1342 |

## FAQ

### Is Qwen2.5 7B Instruct better than Trinity Large Thinking?

Trinity Large Thinking is the stronger model overall, scoring 38.6 to 29.0 on the Noometry Index.

### Which is cheaper, Qwen2.5 7B Instruct or Trinity Large Thinking?

Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Trinity Large Thinking lists at $0.25 and $0.80.

### Is Qwen2.5 7B Instruct or Trinity Large Thinking better for coding?

Qwen2.5 7B Instruct scores higher on coding benchmarks: 36.5 versus 34.1 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do Qwen2.5 7B Instruct and Trinity Large Thinking share?

0 benchmarks have published results for both models. Qwen2.5 7B Instruct has 15 scored results on Noometry and Trinity Large Thinking has 24.