Trinity Large Thinking vs o1
o1 leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 15.2 to 10.8, and Trinity Large Thinking costs 75.0x less per output token ($0.80 vs $60.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
Trinity Large Thinking or o1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for output tokens 75.0x cheaper ($0.80 vs $60.00 per million) and a larger context window (262K against 200K tokens).
- Shared results
- 35
- Trinity Large Thinking only
- 17
- o1 only
- 34
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
o1
o1 leads, 15.2 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Trinity Large Thinking has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
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 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, o1 not benchmarked.
- Widest category
- Agentic work, 27.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Trinity Large Thinking 90.1% against o1 62.6%, 27.5 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against o1 $60.00 per million output tokens, 75.0x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, o1 200K.
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 | o1 | Ties |
|---|---|---|---|---|
| Coding | 1 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| General | 2 | 16 | ||
| All shared boards | 7 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| LMArena WebDev | 1238 | Not measured | Only Trinity Large Thinking measured |
| AA Coding Index | 25.8 | 39.7 | o1 |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| SciCode | 40.6% | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 12.9% | Trinity Large Thinking |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| LMArena Text: coding | 1357 | 1367 | o1 |
| LMArena Text: software and IT | 1362 | 1366 | o1 |
| LMArena WebDev: React | 1231 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: HTML | 1240 | Not measured | Only Trinity Large Thinking measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 15.2 | o1 |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| τ²-Bench Telecom | 90.1% | 62.6% | Trinity Large Thinking |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 75.2% | 74.7% | Trinity Large Thinking |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 7.0% | Trinity Large Thinking |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1342 | 1371 | o1 |
| LMArena Text: expert | 1359 | 1361 | o1 |
| LMArena Text: hard prompts in English | 1356 | 1372 | o1 |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | 1365 | 1388 | o1 |
| LMArena Text: mathematical work | 1350 | 1390 | o1 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| CaseLaw (Vals AI) | 57.9% | Not measured | Only Trinity Large Thinking measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only Trinity Large Thinking measured |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1355 | 1369 | o1 |
| LMArena Text: medicine and healthcare | 1349 | 1355 | o1 |
| LMArena Text: legal and government | 1345 | 1368 | o1 |
| LMArena Text: business and finance | 1329 | 1328 | Trinity Large Thinking |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: UI components | 1042 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: game dev | 1078 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: data viz | 1098 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: 3D | 1081 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: all code categories | 1117 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: SVG | 1016 | 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index216
| Benchmark | Trinity Large Thinking | o1 | Leader |
|---|---|---|---|
| IFBench | 56.3% | 70.3% | o1 |
| Long context reasoning (AA-LCR) | 38.0% | 65.0% | o1 |
| DTBench | Not measured | 74.7% | Only o1 measured |
| LMCA | Not measured | 22.3% | Only o1 measured |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1366 | o1 |
| LMArena Text: instruction following | 1326 | 1367 | o1 |
| LMArena Text: creative writing | 1308 | 1347 | o1 |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | 1356 | 1372 | o1 |
| LMArena Text: non-English | 1325 | 1358 | o1 |
| LMArena Text: Chinese | 1371 | 1394 | o1 |
| LMArena Text: German | 1356 | 1337 | Trinity Large Thinking |
| LMArena Text: Japanese | 1312 | 1346 | o1 |
| LMArena Text: Korean | 1306 | 1396 | o1 |
| LMArena Text: French | 1366 | 1344 | Trinity Large Thinking |
| LMArena Text: Spanish | 1327 | 1345 | o1 |
| LMArena Text: Russian | 1338 | 1356 | o1 |
| LMArena Text: Polish | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: multi-turn | 1320 | 1355 | o1 |
| LMArena Text: longer queries | 1318 | 1378 | o1 |
| LMArena Text: writing and language | 1317 | 1360 | o1 |
| LMArena Text: entertainment and media | 1296 | 1358 | o1 |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | o1 | 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 | OpenAI o1 |
|---|---|---|
| Maker | Arcee AI | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | December 17, 2024 |
| Context window | 262K | 200K |
| Input $/Mtok | $0.25 | $15.00 |
| Output $/Mtok | $0.80 | $60.00 |
| 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 o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Trinity Large Thinking for output tokens 75.0x cheaper ($0.80 vs $60.00 per million) and a larger context window (262K against 200K tokens).
Which is cheaper, Trinity Large Thinking or o1?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Trinity Large Thinking or o1?
Trinity Large Thinking, with 262K tokens against 200K.
How many benchmarks is the Trinity Large Thinking vs o1 comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.