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Trinity Large Thinking vs Muse Spark 1.1

Updated October 21, 2026. 48 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Trinity Large ThinkingArcee AI
Coding
5.4
Overall
10.8
Output $/M
$0.80
0
Muse Spark 1.1Meta
Coding
48.9
Overall
33.7
Output $/M
$4.25
48
vs boards won of 48 shared

Decision reading

Pick Muse Spark 1.1 for coding: it scores 48.9 on the Vibe Coding Index against 5.4 for Trinity Large Thinking.

Pick Trinity Large Thinking when output tokens cost 5.3x less.

Shared results
48
Trinity Large Thinking only
4
Muse Spark 1.1 only
48
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.

At a glance

Coding index
Trinity Large Thinking 5.4 from 1 benchmark, Muse Spark 1.1 48.9 from 1 benchmark.
Widest category
Coding, 36.2 points apart on average over 7 shared benchmarks.
Widest single gap
LMArena WebDev: React: Trinity Large Thinking 1231 against Muse Spark 1.1 1532, 50.2 points apart on a 0 to 100 scale.
Output price
Trinity Large Thinking $0.80 against Muse Spark 1.1 $4.25 per million output tokens, 5.3x apart.
Context window
Trinity Large Thinking takes 262K tokens, Muse Spark 1.1 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.

Boards won, where both have a result
CategoryTrinity Large ThinkingBarsMuse Spark 1.1Ties
Coding07
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge05
Web design08
General019
All shared boards048
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
Coding benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
LMArena WebDev12381542Muse Spark 1.1
AA Coding Index25.871.3Muse Spark 1.1
LiveCodeBench (Vals AI)Not measured85.9%Only Muse Spark 1.1 measured
SWE-bench Verified (Vals AI)Not measured82.0%Only Muse Spark 1.1 measured
Vibe Code Bench (Vals AI)Not measured72.2%Only Muse Spark 1.1 measured
Code migration (Vals AI)Not measured31.1%Only Muse Spark 1.1 measured
ProgramBench (Vals AI)Not measured0.0%Only Muse Spark 1.1 measured
Terminal-Bench 2.1 (Vals AI)Not measured69.3%Only Muse Spark 1.1 measured
SkillsBench (Vals AI)Not measured59.2%Only Muse Spark 1.1 measured
SciCode40.6%58.8%Muse Spark 1.1
Terminal-Bench Hard (Artificial Analysis run)22.7%Not measuredOnly Trinity Large Thinking measured
LMArena Text: coding13571498Muse Spark 1.1
LMArena Text: software and IT13621498Muse Spark 1.1
LMArena WebDev: React12311532Muse Spark 1.1
LMArena WebDev: HTML12401540Muse Spark 1.1
LMArena WebDev: from an imageNot measured1540Only Muse Spark 1.1 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
AA Intelligence Index10.833.7Muse Spark 1.1
Epoch Capabilities IndexNot measured154.1Only Muse Spark 1.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
AA Agentic Index1.025.8Muse Spark 1.1
Design Arena: agents building web appsNot measured1207Only Muse Spark 1.1 measured
Design Arena: agents building full-stack appsNot measured1199Only Muse Spark 1.1 measured
Design Arena: agents building mobile appsNot measured1177Only Muse Spark 1.1 measured
Design Arena: agents building Android appsNot measured1184Only Muse Spark 1.1 measured
Design Arena: agents building Godot gamesNot measured1103Only Muse Spark 1.1 measured
Design Arena: agents building gamesNot measured1183Only Muse Spark 1.1 measured
Design Arena: HTML slidesNot measured1194Only Muse Spark 1.1 measured
Design Arena: PowerPoint slidesNot measured1156Only Muse Spark 1.1 measured
Design Arena: spreadsheetsNot measured974Only Muse Spark 1.1 measured
Finance Agent (Vals AI)Not measured57.2%Only Muse Spark 1.1 measured
Legal Research Bench (Vals AI)Not measured38.0%Only Muse Spark 1.1 measured
Excel Modeling Benchmark (Vals AI)Not measured56.4%Only Muse Spark 1.1 measured
Time Horizon Index (Vals AI)Not measured1.7%Only Muse Spark 1.1 measured
τ²-Bench Telecom90.1%Not measuredOnly Trinity Large Thinking measured
APEX-AgentsNot measured31.8%Only Muse Spark 1.1 measured
Vending-Bench 2Not measured$6,520Only Muse Spark 1.1 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
GPQA DiamondNot measured91.2%Only Muse Spark 1.1 measured
MMMU Pro (Vals AI)Not measured86.6%Only Muse Spark 1.1 measured
GPQA Diamond (Artificial Analysis run)75.2%89.8%Muse Spark 1.1
Humanity's Last Exam (Artificial Analysis run)15.8%46.2%Muse Spark 1.1
Chess puzzlesNot measured34.0%Only Muse Spark 1.1 measured
LMArena Text: hard prompts13421485Muse Spark 1.1
LMArena Text: expert13591478Muse Spark 1.1
LMArena Text: hard prompts in English13561480Muse Spark 1.1
LMArena Vision: diagramsNot measured1305Only Muse Spark 1.1 measured
LMArena Vision: homeworkNot measured1287Only Muse Spark 1.1 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
ProofBenchNot measured39.0%Only Muse Spark 1.1 measured
LMArena Text: math13651482Muse Spark 1.1
LMArena Text: mathematical work13501487Muse Spark 1.1
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
MMLU ProNot measured88.7%Only Muse Spark 1.1 measured
LegalBench (Vals AI)Not measured85.0%Only Muse Spark 1.1 measured
CaseLaw (Vals AI)57.9%Not measuredOnly Trinity Large Thinking measured
CorpFin (Vals AI)54.7%71.3%Muse Spark 1.1
TaxEval (Vals AI)Not measured79.7%Only Muse Spark 1.1 measured
MortgageTax (Vals AI)Not measured66.1%Only Muse Spark 1.1 measured
SimpleQA VerifiedNot measured57.8%Only Muse Spark 1.1 measured
LMArena Text: science13551484Muse Spark 1.1
LMArena Text: medicine and healthcare13491479Muse Spark 1.1
LMArena Text: legal and government13451481Muse Spark 1.1
LMArena Text: business and finance13291470Muse Spark 1.1
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
Design Arena: websites11371270Muse Spark 1.1
Design Arena: UI components10451290Muse Spark 1.1
Design Arena: game dev10791269Muse Spark 1.1
Design Arena: data viz10981268Muse Spark 1.1
Design Arena: 3D10821260Muse Spark 1.1
Design Arena: all code categories11171276Muse Spark 1.1
Design Arena: SVG10161234Muse Spark 1.1
Design Arena: ASCII art10411270Muse Spark 1.1
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
Public Benefits Bench (Vals AI)Not measured61.3%Only Muse Spark 1.1 measured
SAGE (Vals AI)Not measured45.6%Only Muse Spark 1.1 measured
MedScribe (Vals AI)Not measured88.9%Only Muse Spark 1.1 measured
Vals Multimodal IndexNot measured66.7%Only Muse Spark 1.1 measured
IFBench56.3%Not measuredOnly Trinity Large Thinking measured
Long context reasoning (AA-LCR)38.0%77.7%Muse Spark 1.1
DTBenchNot measured94.4%Only Muse Spark 1.1 measured
LMCANot measured49.9%Only Muse Spark 1.1 measured
Surface Evolver Bench15.6%52.5%Muse Spark 1.1
LMArena Text13401478Muse Spark 1.1
LMArena Text: instruction following13261457Muse Spark 1.1
LMArena Text: creative writing13081435Muse Spark 1.1
LMArena VisionNot measured1293Only Muse Spark 1.1 measured
LMArena Text: English13561478Muse Spark 1.1
LMArena Text: non-English13251471Muse Spark 1.1
LMArena Text: Chinese13711515Muse Spark 1.1
LMArena Text: German13561469Muse Spark 1.1
LMArena Text: Japanese13121450Muse Spark 1.1
LMArena Text: Korean13061459Muse Spark 1.1
LMArena Text: French13661492Muse Spark 1.1
LMArena Text: Spanish13271464Muse Spark 1.1
LMArena Text: Russian13381482Muse Spark 1.1
LMArena Text: Polish13121499Muse Spark 1.1
LMArena Text: multi-turn13201484Muse Spark 1.1
LMArena Text: longer queries13181462Muse Spark 1.1
LMArena Text: writing and language13171448Muse Spark 1.1
LMArena Text: entertainment and media12961438Muse Spark 1.1
LMArena Vision: EnglishNot measured1292Only Muse Spark 1.1 measured
LMArena Vision: ChineseNot measured1348Only Muse Spark 1.1 measured
LMArena Vision: reading textNot measured1301Only Muse Spark 1.1 measured
LMArena Vision: humorNot measured1288Only Muse Spark 1.1 measured
LMArena Vision: creative writingNot measured1253Only Muse Spark 1.1 measured
LMArena DocumentNot measured1465Only Muse Spark 1.1 measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Trinity Large Thinking and Muse Spark 1.1
BenchmarkTrinity Large ThinkingMuse Spark 1.1Leader
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$0.0006Fits in one request
Muse Spark 1.1$0.0034Fits in one request

Trinity Large Thinking costs less

Repository review

50K input + 3K output tokens

Trinity Large Thinking$0.015Fits in one request
Muse Spark 1.1$0.075Fits in one request

Trinity Large Thinking costs less

Agent loop

200K input + 10K output tokens

Trinity Large Thinking$0.058Fits in one request
Muse Spark 1.1$0.292Fits in one request

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.

Specifications of Trinity Large Thinking and Muse Spark 1.1
SpecificationArcee AI Trinity Large ThinkingMeta Muse Spark 1.1
MakerArcee AIMeta
WeightsOpen weightsNo public weights listed
ReleasedApril 1, 2026July 16, 2026
Context window262K1.05M
Input $/Mtok$0.25$1.25
Output $/Mtok$0.80$4.25
Coding index5.448.9
Independent measurements behind it11
Cost per solved taskNot computable$0.43
SourceOpenRouter listingOpenRouter listing

Questions

Is Trinity Large Thinking better than Muse Spark 1.1 for coding?

Pick Muse Spark 1.1 for coding: it scores 48.9 on the Vibe Coding Index against 5.4 for Trinity Large Thinking. Pick Trinity Large Thinking when output tokens cost 5.3x less.

Which is cheaper, Trinity Large Thinking or Muse Spark 1.1?

Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Cost per solved task is known for Muse Spark 1.1 only.

Which has the bigger context window, Trinity Large Thinking or Muse Spark 1.1?

Muse Spark 1.1, with 1.05M tokens against 262K.

How many benchmarks is the Trinity Large Thinking vs Muse Spark 1.1 comparison based on?

48 benchmarks have a result for both models as of October 21, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Muse Spark 1.1's on 1. Where only one model has a result, the page says so instead of naming a winner.

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