Trinity Large Thinking vs Muse Wonder
Trinity Large Thinking costs 25.0x less per output token than Muse Wonder ($0.80 vs $20.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
Trinity Large Thinking or Muse Wonder: which to pick
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding.
Pick Trinity Large Thinking for output tokens 25.0x cheaper ($0.80 vs $20.00 per million).
- Shared results
- 35
- Trinity Large Thinking only
- 17
- Muse Wonder only
- 25
- Like-for-like categories
- 6 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
Only Muse Wonder has a coding score, so no call is made.
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
Muse Wonder
Muse Wonder leads, 1362 against 1137.
Same board, both measured
General
Vals Index
No call
Only Muse Wonder has a Vals Index result, 64.9%.
Directional only
Long documents
Listed context window
Muse Wonder
Muse Wonder takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, Muse Wonder $0.014 per turn.
List prices
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, Muse Wonder 71.9 from 3 benchmarks.
- Widest category
- Coding, 42.2 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: Trinity Large Thinking 1231 against Muse Wonder 1641, 61.6 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against Muse Wonder $20.00 per million output tokens, 25.0x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, Muse Wonder 1.05M.
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 | Muse Wonder | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 13 | ||
| All shared boards | 0 | 35 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| LMArena WebDev | 1238 | 1652 | Muse Wonder |
| AA Coding Index | 25.8 | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 49.5% | Only Muse Wonder measured |
| Vibe Code Bench (Vals AI) | Not measured | 83.1% | Only Muse Wonder measured |
| Code migration (Vals AI) | Not measured | 62.5% | Only Muse Wonder measured |
| IOI (Vals AI) | Not measured | 66.9% | Only Muse Wonder measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 45.6% | Only Muse Wonder measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 26.6% | Only Muse Wonder measured |
| CyberBench (Vals AI) | Not measured | 80.4% | Only Muse Wonder measured |
| SciCode | 40.6% | 60.3% | Muse Wonder |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: coding | 1357 | 1522 | Muse Wonder |
| LMArena Text: software and IT | 1362 | 1515 | Muse Wonder |
| LMArena WebDev: React | 1231 | 1641 | Muse Wonder |
| LMArena WebDev: HTML | 1240 | 1694 | Muse Wonder |
| Neither model has a result on the other 24 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | Not measured | Only Trinity Large Thinking measured |
| Epoch Capabilities Index | Not measured | 161.8 | Only Muse Wonder measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| Design Arena: spreadsheets | Not measured | 1243 | Only Muse Wonder measured |
| Finance Agent (Vals AI) | Not measured | 56.6% | Only Muse Wonder measured |
| Tax Agent Bench (Vals AI) | Not measured | 36.3% | Only Muse Wonder measured |
| Legal Research Bench (Vals AI) | Not measured | 50.5% | Only Muse Wonder measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 23.8% | Only Muse Wonder measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 67.1% | Only Muse Wonder measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Muse Wonder measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 22.9% | Only Muse Wonder measured |
| Vals Science Index | Not measured | 39.1% | Only Muse Wonder measured |
| CUA-bench (Vals AI) | Not measured | 7.2% | Only Muse Wonder measured |
| τ²-Bench Telecom | 90.1% | Not measured | Only Trinity Large Thinking measured |
| Vending-Bench 2 | Not measured | $9,432 | Only Muse Wonder measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 27.5% | Only Muse Wonder measured |
| GPQA Diamond (Artificial Analysis run) | 75.2% | Not measured | Only Trinity Large Thinking measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 50.1% | Muse Wonder |
| LMArena Text: hard prompts | 1342 | 1506 | Muse Wonder |
| LMArena Text: expert | 1359 | 1513 | Muse Wonder |
| LMArena Text: hard prompts in English | 1356 | 1506 | Muse Wonder |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| ProofBench | Not measured | 84.0% | Only Muse Wonder measured |
| LMArena Text: math | 1365 | 1486 | Muse Wonder |
| LMArena Text: mathematical work | 1350 | 1514 | Muse Wonder |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Trinity Large Thinking | Muse Wonder | 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 | 1506 | Muse Wonder |
| LMArena Text: medicine and healthcare | 1349 | 1512 | Muse Wonder |
| LMArena Text: legal and government | 1345 | 1505 | Muse Wonder |
| LMArena Text: business and finance | 1329 | 1483 | Muse Wonder |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | 1362 | Muse Wonder |
| Design Arena: UI components | 1042 | 1379 | Muse Wonder |
| Design Arena: game dev | 1078 | 1376 | Muse Wonder |
| Design Arena: data viz | 1098 | 1317 | Muse Wonder |
| Design Arena: 3D | 1081 | 1427 | Muse Wonder |
| Design Arena: all code categories | 1117 | 1371 | Muse Wonder |
| 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 index013
| Benchmark | Trinity Large Thinking | Muse Wonder | Leader |
|---|---|---|---|
| Vals Index | Not measured | 64.9% | Only Muse Wonder measured |
| Public Benefits Bench (Vals AI) | Not measured | 70.2% | Only Muse Wonder measured |
| IFBench | 56.3% | Not measured | Only Trinity Large Thinking measured |
| Long context reasoning (AA-LCR) | 38.0% | 80.7% | Muse Wonder |
| DTBench | Not measured | 95.2% | Only Muse Wonder measured |
| LMCA | Not measured | 56.8% | Only Muse Wonder measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1502 | Muse Wonder |
| LMArena Text: instruction following | 1326 | 1491 | Muse Wonder |
| LMArena Text: creative writing | 1308 | 1491 | Muse Wonder |
| LMArena Text: English | 1356 | 1507 | Muse Wonder |
| LMArena Text: non-English | 1325 | 1492 | Muse Wonder |
| LMArena Text: Chinese | 1371 | 1537 | Muse Wonder |
| 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 | 1523 | Muse Wonder |
| LMArena Text: Spanish | 1327 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Russian | 1338 | 1522 | Muse Wonder |
| LMArena Text: Polish | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: multi-turn | 1320 | 1510 | Muse Wonder |
| LMArena Text: longer queries | 1318 | 1493 | Muse Wonder |
| LMArena Text: writing and language | 1317 | 1496 | Muse Wonder |
| LMArena Text: entertainment and media | 1296 | 1482 | Muse Wonder |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | Muse Wonder | 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 | Meta Muse Wonder |
|---|---|---|
| Maker | Arcee AI | Meta |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | October 12, 2026 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.25 | $4.00 |
| Output $/Mtok | $0.80 | $20.00 |
| Coding index | Not benchmarked | 71.9 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than Muse Wonder for coding?
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Trinity Large Thinking has none, so the two cannot be ranked on coding. Pick Trinity Large Thinking for output tokens 25.0x cheaper ($0.80 vs $20.00 per million).
Which is cheaper, Trinity Large Thinking or Muse Wonder?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Trinity Large Thinking or Muse Wonder?
Muse Wonder, with 1.05M tokens against 262K.
How many benchmarks is the Trinity Large Thinking vs Muse Wonder 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 Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.