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Trinity Large Thinking vs Muse Spark 1.3

Muse Spark 1.3 leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 48.1 to 10.8, and Trinity Large Thinking costs 5.3x less per output token ($0.80 vs $4.25 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Trinity Large ThinkingArcee AI
Coding
No data
Overall
No data
Output $/M
$0.80
0
Muse Spark 1.3Meta
Coding
57.4
Overall
68.6
Output $/M
$4.25
46
vs boards won of 46 shared

Trinity Large Thinking or Muse Spark 1.3: which to pick

Only Muse Spark 1.3 has a coding score, 57.4 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 5.3x cheaper ($0.80 vs $4.25 per million).

Shared results
46
Trinity Large Thinking only
6
Muse Spark 1.3 only
46
Like-for-like categories
8 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.

At a glance

Coding index
Trinity Large Thinking not benchmarked, Muse Spark 1.3 57.4 from 4 benchmarks.
Widest category
Agentic work, 54.5 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: Trinity Large Thinking 1231 against Muse Spark 1.3 1672, 61.6 points apart on a 0 to 100 scale.
Output price
Trinity Large Thinking $0.80 against Muse Spark 1.3 $4.25 per million output tokens, 5.3x apart.
Context window
Trinity Large Thinking takes 262K tokens, Muse Spark 1.3 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 1.3Ties
Coding07
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
Web design08
General018
All shared boards046
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
Coding benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDev12381659Muse Spark 1.3
AA Coding Index25.875.8Muse Spark 1.3
Terminal-Bench 4 (Vals AI)Not measured10.6%Only Muse Spark 1.3 measured
Vibe Code Bench (Vals AI)Not measured82.9%Only Muse Spark 1.3 measured
Code migration (Vals AI)Not measured27.6%Only Muse Spark 1.3 measured
ProgramBench (Vals AI)Not measured0.5%Only Muse Spark 1.3 measured
IOI (Vals AI)Not measured43.9%Only Muse Spark 1.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured72.3%Only Muse Spark 1.3 measured
CyberBench (Vals AI)Not measured69.4%Only Muse Spark 1.3 measured
SciCode40.6%59.7%Muse Spark 1.3
Terminal-Bench Hard (Artificial Analysis run)22.7%Not measuredOnly Trinity Large Thinking measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
ALE-BenchNot measured1688.3Only Muse Spark 1.3 measured
LMArena Text: coding13571510Muse Spark 1.3
LMArena Text: software and IT13621511Muse Spark 1.3
LMArena WebDev: React12311672Muse Spark 1.3
LMArena WebDev: HTML12401653Muse Spark 1.3
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
AA Intelligence Index10.848.1Muse Spark 1.3
Epoch Capabilities IndexNot measured157.2Only Muse Spark 1.3 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
AA Agentic Index1.055.5Muse Spark 1.3
Design Arena: agents building web appsNot measured1297Only Muse Spark 1.3 measured
Design Arena: agents building full-stack appsNot measured1295Only Muse Spark 1.3 measured
Design Arena: agents building mobile appsNot measured1218Only Muse Spark 1.3 measured
Design Arena: agents building Godot gamesNot measured1241Only Muse Spark 1.3 measured
Design Arena: PowerPoint slidesNot measured1219Only Muse Spark 1.3 measured
Design Arena: spreadsheetsNot measured1062Only Muse Spark 1.3 measured
LiveBench researchNot measured68.6%Only Muse Spark 1.3 measured
LiveBench browserNot measured80.0%Only Muse Spark 1.3 measured
LiveBench long agenticNot measured40.4%Only Muse Spark 1.3 measured
Finance Agent (Vals AI)Not measured58.9%Only Muse Spark 1.3 measured
Tax Agent Bench (Vals AI)Not measured36.0%Only Muse Spark 1.3 measured
Legal Research Bench (Vals AI)Not measured40.9%Only Muse Spark 1.3 measured
Excel Modeling Benchmark (Vals AI)Not measured62.7%Only Muse Spark 1.3 measured
Terminal-Bench-Science (Vals AI)Not measured4.3%Only Muse Spark 1.3 measured
τ²-Bench Telecom90.1%Not measuredOnly Trinity Large Thinking measured
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)75.2%94.1%Muse Spark 1.3
Humanity's Last Exam (Artificial Analysis run)15.8%48.7%Muse Spark 1.3
Chess puzzlesNot measured37.0%Only Muse Spark 1.3 measured
Mystery game puzzlesNot measured25.0%Only Muse Spark 1.3 measured
LMArena Text: hard prompts13421503Muse Spark 1.3
LMArena Text: expert13591519Muse Spark 1.3
LMArena Text: hard prompts in English13561500Muse Spark 1.3
LMArena Vision: diagramsNot measured1330Only Muse Spark 1.3 measured
LMArena Vision: homeworkNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
OTIS Mock AIMENot measured99.2%Only Muse Spark 1.3 measured
FrontierMath, tiers 1 to 3Not measured74.4%Only Muse Spark 1.3 measured
FrontierMath, tier 4Not measured46.3%Only Muse Spark 1.3 measured
ProofBenchNot measured58.0%Only Muse Spark 1.3 measured
LMArena Text: math13651497Muse Spark 1.3
LMArena Text: mathematical work13501491Muse Spark 1.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
CaseLaw (Vals AI)57.9%Not measuredOnly Trinity Large Thinking measured
CorpFin (Vals AI)54.7%Not measuredOnly Trinity Large Thinking measured
LMArena Text: science13551507Muse Spark 1.3
LMArena Text: medicine and healthcare13491493Muse Spark 1.3
LMArena Text: legal and government13451499Muse Spark 1.3
LMArena Text: business and finance13291490Muse Spark 1.3
Neither model has a result on the other 12 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 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
Design Arena: websites11371356Muse Spark 1.3
Design Arena: UI components10421349Muse Spark 1.3
Design Arena: game dev10781328Muse Spark 1.3
Design Arena: data viz10981343Muse Spark 1.3
Design Arena: 3D10811358Muse Spark 1.3
Design Arena: all code categories11171352Muse Spark 1.3
Design Arena: SVG10161301Muse Spark 1.3
Design Arena: ASCII art10411278Muse Spark 1.3
LiveBench designNot measured74.8%Only Muse Spark 1.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for Trinity Large Thinking and Muse Spark 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
IFBench56.3%Not measuredOnly Trinity Large Thinking measured
Long context reasoning (AA-LCR)38.0%83.0%Muse Spark 1.3
DTBenchNot measured96.5%Only Muse Spark 1.3 measured
LMCANot measured53.9%Only Muse Spark 1.3 measured
Surface Evolver Bench15.6%Not measuredOnly Trinity Large Thinking measured
LMArena Text13401490Muse Spark 1.3
LMArena Text: instruction following13261479Muse Spark 1.3
LMArena Text: creative writing13081462Muse Spark 1.3
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: English13561495Muse Spark 1.3
LMArena Text: non-English13251481Muse Spark 1.3
LMArena Text: Chinese13711532Muse Spark 1.3
LMArena Text: German13561513Muse Spark 1.3
LMArena Text: Japanese13121470Muse Spark 1.3
LMArena Text: Korean13061490Muse Spark 1.3
LMArena Text: French13661511Muse Spark 1.3
LMArena Text: Spanish13271496Muse Spark 1.3
LMArena Text: Russian13381491Muse Spark 1.3
LMArena Text: Polish13121490Muse Spark 1.3
LMArena Text: multi-turn13201487Muse Spark 1.3
LMArena Text: longer queries13181488Muse Spark 1.3
LMArena Text: writing and language13171470Muse Spark 1.3
LMArena Text: entertainment and media12961457Muse Spark 1.3
LMArena Vision: EnglishNot measured1298Only Muse Spark 1.3 measured
LMArena Vision: ChineseNot measured1384Only Muse Spark 1.3 measured
LMArena Vision: reading textNot measured1319Only Muse Spark 1.3 measured
LMArena Vision: humorNot measured1293Only Muse Spark 1.3 measured
LMArena Vision: creative writingNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 17 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 1.3
BenchmarkTrinity Large ThinkingMuse Spark 1.3Leader
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 1.3$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 1.3$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 1.3$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 1.3
SpecificationArcee AI Trinity Large ThinkingMeta Muse Spark 1.3
MakerArcee AIMeta
WeightsOpen weightsNo public weights listed
ReleasedApril 1, 2026September 2, 2026
Context window262K1.05M
Input $/Mtok$0.25$1.25
Output $/Mtok$0.80$4.25
Coding indexNot benchmarked57.4
Independent measurements behind it14
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Trinity Large Thinking better than Muse Spark 1.3 for coding?

Only Muse Spark 1.3 has a coding score, 57.4 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 5.3x cheaper ($0.80 vs $4.25 per million).

Which is cheaper, Trinity Large Thinking or Muse Spark 1.3?

Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Muse Spark 1.3 costs $1.25 and $4.25. Cost per solved task is known for neither of them.

Which has the bigger context window, Trinity Large Thinking or Muse Spark 1.3?

Muse Spark 1.3, with 1.05M tokens against 262K.

How many benchmarks is the Trinity Large Thinking vs Muse Spark 1.3 comparison based on?

46 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 Spark 1.3's on 4. Where only one model has a result, the page says so instead of naming a winner.

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