Trinity Large Thinking vs DeepSeek V3.1 Terminus
Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Trinity Large Thinking has the edge where output tokens cost 1.3x less, and where the context window is larger, 262K against 164K tokens.
- Shared results
- 37
- Trinity Large Thinking only
- 15
- DeepSeek V3.1 Terminus only
- 7
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 14.8 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 6.8 against 1.0.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 1187 against 1137.
Same board, both measured
Long documents
Listed context window
Trinity Large Thinking
Trinity Large Thinking takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, DeepSeek V3.1 Terminus $0.0008 per turn.
List prices
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, DeepSeek V3.1 Terminus not benchmarked.
- Widest category
- Agentic work, 29.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Trinity Large Thinking 90.1% against DeepSeek V3.1 Terminus 37.1%, 52.9 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against DeepSeek V3.1 Terminus $1.0142 per million output tokens, 1.3x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, DeepSeek V3.1 Terminus 164K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Trinity Large Thinking | Bars | DeepSeek V3.1 Terminus | Ties |
|---|---|---|---|---|
| Coding | 1 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 13 | ||
| All shared boards | 2 | 35 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| LMArena WebDev | 1238 | Not measured | Only Trinity Large Thinking measured |
| AA Coding Index | 25.8 | 43.5 | DeepSeek V3.1 Terminus |
| LiveCodeBench (Artificial Analysis run) | Not measured | 79.8% | Only DeepSeek V3.1 Terminus measured |
| SciCode | 40.6% | 38.0% | Trinity Large Thinking |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 31.8% | DeepSeek V3.1 Terminus |
| ALE-Bench | Not measured | 745.2 | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: coding | 1357 | 1425 | DeepSeek V3.1 Terminus |
| LMArena Text: software and IT | 1362 | 1426 | DeepSeek V3.1 Terminus |
| 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 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 14.8 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 6.8 | DeepSeek V3.1 Terminus |
| τ²-Bench Telecom | 90.1% | 37.1% | Trinity Large Thinking |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 75.2% | 79.2% | DeepSeek V3.1 Terminus |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 16.4% | DeepSeek V3.1 Terminus |
| LMArena Text: hard prompts | 1342 | 1426 | DeepSeek V3.1 Terminus |
| LMArena Text: expert | 1359 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: hard prompts in English | 1356 | 1432 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 89.7 | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 89.7% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: math | 1365 | 1402 | DeepSeek V3.1 Terminus |
| LMArena Text: mathematical work | 1350 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | 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 Pro (Artificial Analysis run) | Not measured | 85.1% | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: science | 1355 | 1451 | DeepSeek V3.1 Terminus |
| LMArena Text: medicine and healthcare | 1349 | 1468 | DeepSeek V3.1 Terminus |
| LMArena Text: legal and government | 1345 | 1457 | DeepSeek V3.1 Terminus |
| LMArena Text: business and finance | 1329 | 1414 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | 1187 | DeepSeek V3.1 Terminus |
| Design Arena: UI components | 1045 | 1182 | DeepSeek V3.1 Terminus |
| Design Arena: game dev | 1079 | 1131 | DeepSeek V3.1 Terminus |
| Design Arena: data viz | 1098 | 1167 | DeepSeek V3.1 Terminus |
| Design Arena: 3D | 1082 | 1141 | DeepSeek V3.1 Terminus |
| Design Arena: all code categories | 1117 | 1175 | DeepSeek V3.1 Terminus |
| Design Arena: SVG | 1017 | 1065 | DeepSeek V3.1 Terminus |
| Design Arena: ASCII art | 1041 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| IFBench | 56.3% | 57.0% | DeepSeek V3.1 Terminus |
| Long context reasoning (AA-LCR) | 38.0% | 69.3% | DeepSeek V3.1 Terminus |
| DTBench | Not measured | 81.3% | Only DeepSeek V3.1 Terminus measured |
| LMCA | Not measured | 28.6% | Only DeepSeek V3.1 Terminus measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1419 | DeepSeek V3.1 Terminus |
| LMArena Text: instruction following | 1326 | 1404 | DeepSeek V3.1 Terminus |
| LMArena Text: creative writing | 1308 | 1403 | DeepSeek V3.1 Terminus |
| LMArena Text: English | 1356 | 1429 | DeepSeek V3.1 Terminus |
| LMArena Text: non-English | 1325 | 1408 | DeepSeek V3.1 Terminus |
| 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 | 1435 | DeepSeek V3.1 Terminus |
| LMArena Text: Polish | 1312 | 1427 | DeepSeek V3.1 Terminus |
| LMArena Text: multi-turn | 1320 | 1411 | DeepSeek V3.1 Terminus |
| LMArena Text: longer queries | 1318 | 1422 | DeepSeek V3.1 Terminus |
| LMArena Text: writing and language | 1317 | 1409 | DeepSeek V3.1 Terminus |
| LMArena Text: entertainment and media | 1296 | 1391 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | DeepSeek V3.1 Terminus | Leader |
|---|---|---|---|
| Neither model has a result on 5 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
Trinity Large Thinking costs less
Repository review
50K input + 3K output tokens
Trinity Large Thinking costs less
Agent loop
200K input + 10K output tokens
Trinity Large Thinking 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 | DeepSeek V3.1 Terminus |
|---|---|---|
| Maker | Arcee AI | DeepSeek |
| Weights | Open weights | Open weights |
| Released | April 1, 2026 | September 22, 2025 |
| Context window | 262K | 164K |
| Input $/Mtok | $0.25 | $0.3063 |
| Output $/Mtok | $0.80 | $1.0142 |
| 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 DeepSeek V3.1 Terminus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Trinity Large Thinking has the edge where output tokens cost 1.3x less, and where the context window is larger, 262K against 164K tokens.
Which is cheaper, Trinity Large Thinking or DeepSeek V3.1 Terminus?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; DeepSeek V3.1 Terminus costs $0.3063 and $1.0142. Cost per solved task is known for neither of them.
Which has the bigger context window, Trinity Large Thinking or DeepSeek V3.1 Terminus?
Trinity Large Thinking, with 262K tokens against 164K.
How many benchmarks is the Trinity Large Thinking vs DeepSeek V3.1 Terminus comparison based on?
37 benchmarks have a result for both models as of October 21, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and DeepSeek V3.1 Terminus's on 0. Where only one model has a result, the page says so instead of naming a winner.