Trinity Large Thinking vs Command R (08-2024)
Updated October 21, 2026. 25 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 the context window is larger, 262K against 128K tokens.
- Shared results
- 25
- Trinity Large Thinking only
- 27
- Command R (08-2024) only
- 0
- Like-for-like categories
- 5 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
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R (08-2024)
Trinity Large Thinking $0.0006, Command R (08-2024) $0.0004 per turn.
List prices
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, Command R (08-2024) not benchmarked.
- Widest category
- Reasoning, 28.0 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Text: expert: Trinity Large Thinking 1359 against Command R (08-2024) 1173, 30.9 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against Command R (08-2024) $0.60 per million output tokens, 1.3x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, Command R (08-2024) 128K.
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 | Command R (08-2024) | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 25 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 | 1187 | Trinity Large Thinking |
| LMArena Text: software and IT | 1362 | 1197 | Trinity Large Thinking |
| 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 index1 board, none shared
| Benchmark | Trinity Large Thinking | Command R (08-2024) | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 |
| LMArena Text: hard prompts | 1342 | 1186 | Trinity Large Thinking |
| LMArena Text: expert | 1359 | 1173 | Trinity Large Thinking |
| LMArena Text: hard prompts in English | 1356 | 1194 | Trinity Large Thinking |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Trinity Large Thinking | Command R (08-2024) | Leader |
|---|---|---|---|
| LMArena Text: math | 1365 | 1188 | Trinity Large Thinking |
| LMArena Text: mathematical work | 1350 | 1198 | Trinity Large Thinking |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 |
| LMArena Text: science | 1355 | 1252 | Trinity Large Thinking |
| LMArena Text: medicine and healthcare | 1349 | 1238 | Trinity Large Thinking |
| LMArena Text: legal and government | 1345 | 1268 | Trinity Large Thinking |
| LMArena Text: business and finance | 1329 | 1215 | Trinity Large Thinking |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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 |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1229 | Trinity Large Thinking |
| LMArena Text: instruction following | 1326 | 1197 | Trinity Large Thinking |
| LMArena Text: creative writing | 1308 | 1234 | Trinity Large Thinking |
| LMArena Text: English | 1356 | 1238 | Trinity Large Thinking |
| LMArena Text: non-English | 1325 | 1216 | Trinity Large Thinking |
| LMArena Text: Chinese | 1371 | 1227 | Trinity Large Thinking |
| LMArena Text: German | 1356 | 1216 | Trinity Large Thinking |
| LMArena Text: Japanese | 1312 | 1166 | Trinity Large Thinking |
| LMArena Text: Korean | 1306 | 1162 | Trinity Large Thinking |
| 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 | 1227 | Trinity Large Thinking |
| LMArena Text: Polish | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: multi-turn | 1320 | 1213 | Trinity Large Thinking |
| LMArena Text: longer queries | 1318 | 1230 | Trinity Large Thinking |
| LMArena Text: writing and language | 1317 | 1241 | Trinity Large Thinking |
| LMArena Text: entertainment and media | 1296 | 1223 | Trinity Large Thinking |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | Command R (08-2024) | 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
Command R (08-2024) costs less
Repository review
50K input + 3K output tokens
Command R (08-2024) costs less
Agent loop
200K input + 10K output tokens
Command R (08-2024) 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 | Cohere Command R (08-2024) |
|---|---|---|
| Maker | Arcee AI | Cohere |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | August 30, 2024 |
| Context window | 262K | 128K |
| Input $/Mtok | $0.25 | $0.15 |
| Output $/Mtok | $0.80 | $0.60 |
| 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 Command R (08-2024) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Trinity Large Thinking has the edge where the context window is larger, 262K against 128K tokens.
Which is cheaper, Trinity Large Thinking or Command R (08-2024)?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Command R (08-2024) costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Trinity Large Thinking or Command R (08-2024)?
Trinity Large Thinking, with 262K tokens against 128K.
How many benchmarks is the Trinity Large Thinking vs Command R (08-2024) comparison based on?
25 benchmarks have a result for both models as of October 21, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Command R (08-2024)'s on 0. Where only one model has a result, the page says so instead of naming a winner.