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Trinity Large Thinking vs DeepSeek V3.2

DeepSeek V3.2 leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 21.5 to 10.8, at about the same output price ($0.88 vs $0.80 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace Trinity Large Thinking Replace DeepSeek V3.2
Trinity Large ThinkingArcee AI
Coding
No data
Overall
No data
Output $/M
$0.80
2
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
47
vs boards won of 49 shared

Trinity Large Thinking or DeepSeek V3.2: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 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 1.1x cheaper ($0.80 vs $0.88 per million) and a larger context window (262K against 164K tokens).

Model ATrinity Large ThinkingArcee AINot benchmarked1 independent benchmarkNo cost per solved taskModel BDeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved task
Shared results
49
Trinity Large Thinking only
3
DeepSeek V3.2 only
26
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, DeepSeek V3.2 30.0 from 3 benchmarks.
Widest category
Coding, 17.3 points apart on average over 7 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): Trinity Large Thinking 38.0% against DeepSeek V3.2 73.3%, 35.3 points apart on a 0 to 100 scale.
Output price
Trinity Large Thinking $0.80 against DeepSeek V3.2 $0.88 per million output tokens, 1.1x apart.
Context window
Trinity Large Thinking takes 262K tokens, DeepSeek V3.2 164K.

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 ThinkingBarsDeepSeek V3.2Ties
Coding07
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge24
Web design08
General019
All shared boards247
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
Coding benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
SWE-bench VerifiedNot measured70.0%Only DeepSeek V3.2 measured
SWE-bench MultilingualNot measured59.0%Only DeepSeek V3.2 measured
LMArena WebDev12381362DeepSeek V3.2
AA Coding Index25.844.2DeepSeek V3.2
LiveCodeBench (Vals AI)Not measured80.7%Only DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)Not measured67.6%Only DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)Not measured5.1%Only DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)Not measured86.2%Only DeepSeek V3.2 measured
SciCode40.6%Not measuredOnly Trinity Large Thinking measured
Terminal-Bench Hard (Artificial Analysis run)22.7%35.6%DeepSeek V3.2
Terminal-BenchNot measured39.6%Only DeepSeek V3.2 measured
LMArena Text: coding13571453DeepSeek V3.2
LMArena Text: software and IT13621449DeepSeek V3.2
LMArena WebDev: React12311350DeepSeek V3.2
LMArena WebDev: HTML12401371DeepSeek V3.2
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
AA Intelligence Index10.821.5DeepSeek V3.2
Epoch Capabilities IndexNot measured146.1Only DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
AA Agentic Index1.0Not measuredOnly Trinity Large Thinking measured
τ²-Bench Telecom90.1%90.6%DeepSeek V3.2
APEX-AgentsNot measured21.3%Only DeepSeek V3.2 measured
Vending-Bench 2Not measured$1,034Only DeepSeek V3.2 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
GPQA DiamondNot measured80.3%Only DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)75.2%84.0%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)15.8%24.6%DeepSeek V3.2
ARC-AGINot measured57.0%Only DeepSeek V3.2 measured
ARC-AGI-2Not measured4.0%Only DeepSeek V3.2 measured
LMArena Text: hard prompts13421434DeepSeek V3.2
LMArena Text: expert13591436DeepSeek V3.2
LMArena Text: hard prompts in English13561446DeepSeek V3.2
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
AIMENot measured84.6%Only DeepSeek V3.2 measured
MGSM (Vals AI)Not measured86.0%Only DeepSeek V3.2 measured
AA Math IndexNot measured92.0Only DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)Not measured92.0%Only DeepSeek V3.2 measured
LMArena Text: math13651435DeepSeek V3.2
LMArena Text: mathematical work13501440DeepSeek V3.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
Knowledge benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
MMLU ProNot measured84.9%Only DeepSeek V3.2 measured
LegalBench (Vals AI)Not measured76.1%Only DeepSeek V3.2 measured
CaseLaw (Vals AI)57.9%55.4%Trinity Large Thinking
CorpFin (Vals AI)54.7%51.0%Trinity Large Thinking
TaxEval (Vals AI)Not measured68.2%Only DeepSeek V3.2 measured
MedQA (Vals AI)Not measured93.9%Only DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)Not measured86.2%Only DeepSeek V3.2 measured
LMArena Text: science13551446DeepSeek V3.2
LMArena Text: medicine and healthcare13491440DeepSeek V3.2
LMArena Text: legal and government13451425DeepSeek V3.2
LMArena Text: business and finance13291410DeepSeek V3.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
Design Arena: websites11371175DeepSeek V3.2
Design Arena: UI components10421146DeepSeek V3.2
Design Arena: game dev10781131DeepSeek V3.2
Design Arena: data viz10981158DeepSeek V3.2
Design Arena: 3D10811132DeepSeek V3.2
Design Arena: all code categories11171163DeepSeek V3.2
Design Arena: SVG10161032DeepSeek V3.2
Design Arena: ASCII art10411083DeepSeek V3.2
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 DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
IFBench56.3%60.7%DeepSeek V3.2
Long context reasoning (AA-LCR)38.0%73.3%DeepSeek V3.2
DTBenchNot measured85.6%Only DeepSeek V3.2 measured
LMCANot measured28.8%Only DeepSeek V3.2 measured
CL-benchNot measured12.4%Only DeepSeek V3.2 measured
Surface Evolver Bench15.6%Not measuredOnly Trinity Large Thinking measured
CL-bench LifeNot measured7.4%Only DeepSeek V3.2 measured
LMArena Text13401425DeepSeek V3.2
LMArena Text: instruction following13261413DeepSeek V3.2
LMArena Text: creative writing13081400DeepSeek V3.2
LMArena Text: English13561439DeepSeek V3.2
LMArena Text: non-English13251409DeepSeek V3.2
LMArena Text: Chinese13711455DeepSeek V3.2
LMArena Text: German13561413DeepSeek V3.2
LMArena Text: Japanese13121374DeepSeek V3.2
LMArena Text: Korean13061370DeepSeek V3.2
LMArena Text: French13661429DeepSeek V3.2
LMArena Text: Spanish13271424DeepSeek V3.2
LMArena Text: Russian13381424DeepSeek V3.2
LMArena Text: Polish13121419DeepSeek V3.2
LMArena Text: multi-turn13201427DeepSeek V3.2
LMArena Text: longer queries13181428DeepSeek V3.2
LMArena Text: writing and language13171407DeepSeek V3.2
LMArena Text: entertainment and media12961395DeepSeek V3.2
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
Image generation benchmarks for Trinity Large Thinking and DeepSeek V3.2
BenchmarkTrinity Large ThinkingDeepSeek V3.2Leader
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
DeepSeek V3.2$0.0007Fits in one request

Trinity Large Thinking costs less

Repository review

50K input + 3K output tokens

Trinity Large Thinking$0.015Fits in one request
DeepSeek V3.2$0.017Fits in one request

Trinity Large Thinking costs less

Agent loop

200K input + 10K output tokens

Trinity Large Thinking$0.058Fits in one request
DeepSeek V3.2$0.067Exceeds the context window

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 DeepSeek V3.2
SpecificationArcee AI Trinity Large ThinkingDeepSeek V3.2
MakerArcee AIDeepSeek
WeightsOpen weightsOpen weights
ReleasedApril 1, 2026December 1, 2025
Context window262K164K
Input $/Mtok$0.25$0.29
Output $/Mtok$0.80$0.88
Coding indexNot benchmarked30.0
Independent measurements behind it13
Cost per solved taskNot computable$0.99
SourceOpenRouter listingOpenRouter listing

Questions

Is Trinity Large Thinking better than DeepSeek V3.2 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 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 1.1x cheaper ($0.80 vs $0.88 per million) and a larger context window (262K against 164K tokens).

Which is cheaper, Trinity Large Thinking or DeepSeek V3.2?

Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; DeepSeek V3.2 costs $0.29 and $0.88. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, Trinity Large Thinking or DeepSeek V3.2?

Trinity Large Thinking, with 262K tokens against 164K.

How many benchmarks is the Trinity Large Thinking vs DeepSeek V3.2 comparison based on?

49 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and DeepSeek V3.2's on 3. Where only one model has a result, the page says so instead of naming a winner.

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