Model comparison
Qwen2.5 7B Instruct vs Trinity Large Thinking
Trinity Large Thinking is the stronger model overall, scoring 38.1 to 28.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)
Arcee AISummary
- The widest gap is in knowledge, where Trinity Large Thinking leads 40.3 to 17.0.
- 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.
Side by side
| Qwen2.5 7B Instruct | Trinity Large Thinking | |
|---|---|---|
| Provider | Alibaba (Qwen) | Arcee AI |
| Noometry Index | 28.6 | 38.1 |
| Released | 2024-09 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 8K | 80K |
| Input $ / M tokens | $0.17 | $0.25 |
| Output $ / M tokens | $0.70 | $0.80 |
| Results tracked | 15 | 24 |
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Category by category
Coding Qwen2.5 7B Instruct leads
Qwen2.5 7B Instruct: 35.9 (#204), Trinity Large Thinking: 33.8 (#238)
| 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 Not comparable
Qwen2.5 7B Instruct: 23.5 (#117), Trinity Large Thinking: —
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| BALROG | 7.8% | — |
Reasoning Trinity Large Thinking leads
Qwen2.5 7B Instruct: 14.3 (#317), Trinity Large Thinking: 16.3 (#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 | 118.44 | — |
Math Trinity Large Thinking leads
Qwen2.5 7B Instruct: 11.8 (#300), Trinity Large Thinking: 34.9 (#149)
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.5% | — |
| Omni-MATH | 29.4% | — |
| LMArena Math | — | 1366 |
Knowledge Trinity Large Thinking leads
Qwen2.5 7B Instruct: 17.0 (#281), Trinity Large Thinking: 40.3 (#107)
| 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 | — | 1359 |
| MMLU | 72.9% | — |
Multilingual Not comparable
Qwen2.5 7B Instruct: —, Trinity Large Thinking: 46.4 (#155)
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | — | 1325 |
| LMArena Chinese | — | 1372 |
| LMArena French | — | 1374 |
| LMArena German | — | 1356 |
| LMArena Japanese | — | 1312 |
| LMArena Korean | — | 1304 |
| LMArena Russian | — | 1338 |
| LMArena Spanish | — | 1356 |
Instruction Following Trinity Large Thinking leads
Qwen2.5 7B Instruct: 63.4 (#226), Trinity Large Thinking: 70.7 (#157)
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| IFEval | 74.1% | — |
| LMArena Instruction Following | — | 1334 |
Long Context Not comparable
Qwen2.5 7B Instruct: —, Trinity Large Thinking: 41.2 (#139)
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | — | 1355 |
Writing & Preference Trinity Large Thinking leads
Qwen2.5 7B Instruct: 48.8 (#194), Trinity Large Thinking: 54.0 (#152)
| Benchmark | Qwen2.5 7B Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1321 |
| WildBench | 73.1% | — |
| LMArena Multi-Turn | — | 1342 |
Frequently asked questions
Is Qwen2.5 7B Instruct better than Trinity Large Thinking?
Trinity Large Thinking is the stronger model overall, scoring 38.1 to 28.6 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: 35.9 versus 33.8 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.