Trinity Large Thinking vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 4.0x less per output token than Trinity Large Thinking ($0.20 vs $0.80 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 10.8
- Output $/M
- $0.80
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
Trinity Large Thinking or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for a larger context window (262K against 33K tokens).
No benchmark has tested both yet. Trinity Large Thinking has results on LMArena WebDev, AA Coding Index and SciCode; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Trinity Large Thinking has a AA Intelligence Index result, 10.8.
Directional only
Agentic work
AA Agentic Index
No call
Only Trinity Large Thinking has a AA Agentic Index result, 1.0.
Directional only
Web design
Design Arena: websites
No call
Only Trinity Large Thinking has a Design Arena: websites result, 1137.
Directional only
Long documents
Listed context window
Trinity Large Thinking
Trinity Large Thinking takes 262K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
Trinity Large Thinking $0.0006, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Trinity Large Thinking $0.80 against Qwen2.5 7B Instruct $0.20 per million output tokens, 4.0x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, Qwen2.5 7B Instruct 33K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LMArena WebDev | 1238 | Not measured | Only Trinity Large Thinking measured |
| AA Coding Index | 25.8 | Not measured | Only Trinity Large Thinking measured |
| SciCode | 40.6% | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: coding | 1357 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: software and IT | 1362 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: React | 1231 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: HTML | 1240 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | Not measured | Only Trinity Large Thinking measured |
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| τ²-Bench Telecom | 90.1% | Not measured | Only Trinity Large Thinking measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 75.2% | Not measured | Only Trinity Large Thinking measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | Not measured | Only Trinity Large Thinking measured |
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: hard prompts | 1342 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: expert | 1359 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: hard prompts in English | 1356 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: math | 1365 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: mathematical work | 1350 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 57.9% | Not measured | Only Trinity Large Thinking measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only Trinity Large Thinking measured |
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: science | 1355 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: legal and government | 1345 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: business and finance | 1329 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: UI components | 1045 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: game dev | 1079 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: data viz | 1098 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: 3D | 1082 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: all code categories | 1117 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: SVG | 1017 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: ASCII art | 1041 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | Trinity Large Thinking | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 56.3% | Not measured | Only Trinity Large Thinking measured |
| Long context reasoning (AA-LCR) | 38.0% | Not measured | Only Trinity Large Thinking measured |
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: instruction following | 1326 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: creative writing | 1308 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: English | 1356 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: non-English | 1325 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Chinese | 1371 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: German | 1356 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Japanese | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Korean | 1306 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: French | 1366 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Spanish | 1327 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Russian | 1338 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Polish | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: multi-turn | 1320 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: longer queries | 1318 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: writing and language | 1317 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: entertainment and media | 1296 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 24 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B Instruct costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Arcee AI Trinity Large Thinking | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | Arcee AI | Qwen |
| Weights | Open weights | Open weights |
| Released | April 1, 2026 | October 16, 2024 |
| Context window | 262K | 33K |
| Input $/Mtok | $0.25 | $0.10 |
| Output $/Mtok | $0.80 | $0.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Trinity Large Thinking for a larger context window (262K against 33K tokens).
Which is cheaper, Trinity Large Thinking or Qwen2.5 7B Instruct?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Trinity Large Thinking or Qwen2.5 7B Instruct?
Trinity Large Thinking, with 262K tokens against 33K.
Has any benchmark tested both Trinity Large Thinking and Qwen2.5 7B Instruct?
No benchmark has tested both yet. Trinity Large Thinking has results on LMArena WebDev, AA Coding Index and SciCode; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 21, 2026.