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Trinity Large Thinking vs Mistral Medium 3.1

Trinity Large Thinking leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 10.8 to 9.2, and Trinity Large Thinking costs 2.5x less per output token ($0.80 vs $2.00 per million). Updated October 21, 2026; 47 benchmarks have a result for both.

Replace Trinity Large Thinking Replace Mistral Medium 3.1
Trinity Large ThinkingArcee AI
Coding
No data
Overall
10.8
Output $/M
$0.80
14
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
9.2
Output $/M
$2.00
33
vs boards won of 47 shared

Trinity Large Thinking or Mistral Medium 3.1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Trinity Large Thinking for output tokens 2.5x cheaper ($0.80 vs $2.00 per million) and a larger context window (262K against 131K tokens).

Model ATrinity Large ThinkingArcee AINot benchmarked1 independent benchmarkNo cost per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
47
Trinity Large Thinking only
5
Mistral Medium 3.1 only
27
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, Mistral Medium 3.1 not benchmarked.
Widest category
Agentic work, 25.2 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Trinity Large Thinking 90.1% against Mistral Medium 3.1 40.6%, 49.4 points apart on a 0 to 100 scale.
Output price
Trinity Large Thinking $0.80 against Mistral Medium 3.1 $2.00 per million output tokens, 2.5x apart.
Context window
Trinity Large Thinking takes 262K tokens, Mistral Medium 3.1 131K.

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 ThinkingBarsMistral Medium 3.1Ties
Coding32
Intelligence10
Agentic work11
Reasoning23
Math02
Knowledge14
Web design53
General118
All shared boards1433
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
Coding benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
LMArena WebDev1238Not measuredOnly Trinity Large Thinking measured
AA Coding Index25.820.5Trinity Large Thinking
LiveCodeBench (Vals AI)Not measured44.8%Only Mistral Medium 3.1 measured
LiveCodeBench (Artificial Analysis run)Not measured40.6%Only Mistral Medium 3.1 measured
SciCode40.6%32.4%Trinity Large Thinking
Terminal-Bench Hard (Artificial Analysis run)22.7%10.6%Trinity Large Thinking
ALE-BenchNot measured210.2Only Mistral Medium 3.1 measured
LMArena Text: coding13571433Mistral Medium 3.1
LMArena Text: software and IT13621436Mistral Medium 3.1
LMArena WebDev: React1231Not measuredOnly Trinity Large Thinking measured
LMArena WebDev: HTML1240Not measuredOnly Trinity Large Thinking measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
AA Intelligence Index10.89.2Trinity Large Thinking
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
Agentic work benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
AA Agentic Index1.01.9Mistral Medium 3.1
τ²-Bench Telecom90.1%40.6%Trinity Large Thinking
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
Reasoning benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
MMMU Pro (Vals AI)Not measured63.0%Only Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)75.2%58.8%Trinity Large Thinking
Humanity's Last Exam (Artificial Analysis run)15.8%4.7%Trinity Large Thinking
LMArena Text: hard prompts13421426Mistral Medium 3.1
LMArena Text: expert13591408Mistral Medium 3.1
LMArena Text: hard prompts in English13561435Mistral Medium 3.1
LMArena Vision: diagramsNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: homeworkNot measured1165Only Mistral Medium 3.1 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
AIMENot measured42.3%Only Mistral Medium 3.1 measured
MATH 500 (Vals AI)Not measured87.0%Only Mistral Medium 3.1 measured
MGSM (Vals AI)Not measured90.9%Only Mistral Medium 3.1 measured
AA Math IndexNot measured38.3Only Mistral Medium 3.1 measured
AIME 2025 (Artificial Analysis run)Not measured38.3%Only Mistral Medium 3.1 measured
LMArena Text: math13651408Mistral Medium 3.1
LMArena Text: mathematical work13501412Mistral Medium 3.1
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
Knowledge benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.1 measured
LegalBench (Vals AI)Not measured62.0%Only Mistral Medium 3.1 measured
CaseLaw (Vals AI)57.9%Not measuredOnly Trinity Large Thinking measured
CorpFin (Vals AI)54.7%50.7%Trinity Large Thinking
TaxEval (Vals AI)Not measured70.3%Only Mistral Medium 3.1 measured
MortgageTax (Vals AI)Not measured36.5%Only Mistral Medium 3.1 measured
MedQA (Vals AI)Not measured78.2%Only Mistral Medium 3.1 measured
MMLU Pro (Artificial Analysis run)Not measured68.3%Only Mistral Medium 3.1 measured
LMArena Text: science13551444Mistral Medium 3.1
LMArena Text: medicine and healthcare13491449Mistral Medium 3.1
LMArena Text: legal and government13451439Mistral Medium 3.1
LMArena Text: business and finance13291418Mistral Medium 3.1
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index53
Web design benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
Design Arena: websites11371133Trinity Large Thinking
Design Arena: UI components10451100Mistral Medium 3.1
Design Arena: game dev10791074Trinity Large Thinking
Design Arena: data viz10981144Mistral Medium 3.1
Design Arena: 3D10821081Trinity Large Thinking
Design Arena: all code categories11171120Mistral Medium 3.1
Design Arena: SVG1017993Trinity Large Thinking
Design Arena: ASCII art1041997Trinity Large Thinking
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
General benchmarks for Trinity Large Thinking and Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
IFBench56.3%39.8%Trinity Large Thinking
Long context reasoning (AA-LCR)38.0%42.7%Mistral Medium 3.1
DTBenchNot measured67.5%Only Mistral Medium 3.1 measured
LMCANot measured19.1%Only Mistral Medium 3.1 measured
Surface Evolver Bench15.6%Not measuredOnly Trinity Large Thinking measured
LMArena Text13401424Mistral Medium 3.1
LMArena Text: instruction following13261398Mistral Medium 3.1
LMArena Text: creative writing13081391Mistral Medium 3.1
LMArena VisionNot measured1172Only Mistral Medium 3.1 measured
LMArena Text: English13561438Mistral Medium 3.1
LMArena Text: non-English13251408Mistral Medium 3.1
LMArena Text: Chinese13711447Mistral Medium 3.1
LMArena Text: German13561432Mistral Medium 3.1
LMArena Text: Japanese13121379Mistral Medium 3.1
LMArena Text: Korean13061380Mistral Medium 3.1
LMArena Text: French13661460Mistral Medium 3.1
LMArena Text: Spanish13271433Mistral Medium 3.1
LMArena Text: Russian13381411Mistral Medium 3.1
LMArena Text: Polish13121430Mistral Medium 3.1
LMArena Text: multi-turn13201418Mistral Medium 3.1
LMArena Text: longer queries13181406Mistral Medium 3.1
LMArena Text: writing and language13171395Mistral Medium 3.1
LMArena Text: entertainment and media12961390Mistral Medium 3.1
LMArena Vision: EnglishNot measured1179Only Mistral Medium 3.1 measured
LMArena Vision: ChineseNot measured1190Only Mistral Medium 3.1 measured
LMArena Vision: reading textNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: humorNot measured1168Only Mistral Medium 3.1 measured
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writingNot measured1200Only Mistral Medium 3.1 measured
Neither model has a result on the other 16 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 Mistral Medium 3.1
BenchmarkTrinity Large ThinkingMistral Medium 3.1Leader
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
Mistral Medium 3.1$0.0014Fits in one request

Trinity Large Thinking costs less

Repository review

50K input + 3K output tokens

Trinity Large Thinking$0.015Fits in one request
Mistral Medium 3.1$0.026Fits in one request

Trinity Large Thinking costs less

Agent loop

200K input + 10K output tokens

Trinity Large Thinking$0.058Fits in one request
Mistral Medium 3.1$0.100Exceeds 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 Mistral Medium 3.1
SpecificationArcee AI Trinity Large ThinkingMistral Medium 3.1
MakerArcee AIMistral AI
WeightsOpen weightsNo public weights listed
ReleasedApril 1, 2026August 13, 2025
Context window262K131K
Input $/Mtok$0.25$0.40
Output $/Mtok$0.80$2.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Trinity Large Thinking better than Mistral Medium 3.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Trinity Large Thinking for output tokens 2.5x cheaper ($0.80 vs $2.00 per million) and a larger context window (262K against 131K tokens).

Which is cheaper, Trinity Large Thinking or Mistral Medium 3.1?

Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Trinity Large Thinking or Mistral Medium 3.1?

Trinity Large Thinking, with 262K tokens against 131K.

How many benchmarks is the Trinity Large Thinking vs Mistral Medium 3.1 comparison based on?

47 benchmarks have a result for both models as of October 21, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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