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Trinity Large Thinking vs Mistral Medium 3.5

Mistral Medium 3.5 leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 14.2 to 10.8, and Trinity Large Thinking costs 9.4x less per output token ($0.80 vs $7.50 per million). Updated October 22, 2026; 51 benchmarks have a result for both.

Replace Trinity Large Thinking Replace Mistral Medium 3.5
Trinity Large ThinkingArcee AI
Coding
No data
Overall
No data
Output $/M
$0.80
6
Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
37.3
Output $/M
$7.50
45
vs boards won of 51 shared

Trinity Large Thinking or Mistral Medium 3.5: which to pick

Only Mistral Medium 3.5 has a coding score, 25.5 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 9.4x cheaper ($0.80 vs $7.50 per million) and work measured by SciCode or GPQA Diamond (Artificial Analysis run), where it leads.

Model ATrinity Large ThinkingArcee AINot benchmarked1 independent benchmarkNo cost per solved taskModel BMistral Medium 3.5Mistral AI25.5/1003 independent benchmarks$6.02 per solved task
Shared results
51
Trinity Large Thinking only
1
Mistral Medium 3.5 only
30
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 3.5 25.5 from 3 benchmarks.
Widest category
General, 14.6 points apart on average over 19 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): Trinity Large Thinking 38.0% against Mistral Medium 3.5 69.3%, 31.3 points apart on a 0 to 100 scale.
Output price
Trinity Large Thinking $0.80 against Mistral Medium 3.5 $7.50 per million output tokens, 9.4x apart.
Context window
Both take 262K tokens.

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 3.5Ties
Coding17
Intelligence01
Agentic work02
Reasoning23
Math02
Knowledge15
Web design26
General019
All shared boards645
CodingLike-for-likeSix of these boards feed the Vibe Coding Index17
Coding benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
LMArena WebDev12381264Mistral Medium 3.5
AA Coding Index25.846.9Mistral Medium 3.5
SWE-bench Verified (Vals AI)Not measured66.4%Only Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)Not measured2.9%Only Mistral Medium 3.5 measured
Code migration (Vals AI)Not measured5.1%Only Mistral Medium 3.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured39.0%Only Mistral Medium 3.5 measured
SciCode40.6%40.2%Trinity Large Thinking
Terminal-Bench Hard (Artificial Analysis run)22.7%33.3%Mistral Medium 3.5
ALE-BenchNot measured764.0Only Mistral Medium 3.5 measured
LMArena Text: coding13571460Mistral Medium 3.5
LMArena Text: software and IT13621458Mistral Medium 3.5
LMArena WebDev: React12311254Mistral Medium 3.5
LMArena WebDev: HTML12401308Mistral Medium 3.5
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
AA Intelligence Index10.814.2Mistral Medium 3.5
Epoch Capabilities IndexNot measured141.4Only Mistral Medium 3.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
AA Agentic Index1.07.0Mistral Medium 3.5
Finance Agent (Vals AI)Not measured32.1%Only Mistral Medium 3.5 measured
Tax Agent Bench (Vals AI)Not measured4.5%Only Mistral Medium 3.5 measured
Legal Research Bench (Vals AI)Not measured9.1%Only Mistral Medium 3.5 measured
Excel Modeling Benchmark (Vals AI)Not measured11.2%Only Mistral Medium 3.5 measured
τ²-Bench Telecom90.1%94.2%Mistral Medium 3.5
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
Reasoning benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
GPQA DiamondNot measured34.9%Only Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)75.2%74.8%Trinity Large Thinking
Humanity's Last Exam (Artificial Analysis run)15.8%13.8%Trinity Large Thinking
LMArena Text: hard prompts13421436Mistral Medium 3.5
LMArena Text: expert13591432Mistral Medium 3.5
LMArena Text: hard prompts in English13561445Mistral Medium 3.5
LMArena Vision: diagramsNot measured1254Only Mistral Medium 3.5 measured
LMArena Vision: homeworkNot measured1230Only Mistral Medium 3.5 measured
Kagi LLM BenchmarkNot measured41.4%Only Mistral Medium 3.5 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 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
ProofBenchNot measured9.0%Only Mistral Medium 3.5 measured
LMArena Text: math13651431Mistral Medium 3.5
LMArena Text: mathematical work13501440Mistral Medium 3.5
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
Knowledge benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.5 measured
CaseLaw (Vals AI)57.9%44.2%Trinity Large Thinking
CorpFin (Vals AI)54.7%58.8%Mistral Medium 3.5
TaxEval (Vals AI)Not measured68.0%Only Mistral Medium 3.5 measured
MortgageTax (Vals AI)Not measured28.9%Only Mistral Medium 3.5 measured
MedCode (Vals AI)Not measured33.8%Only Mistral Medium 3.5 measured
LMArena Text: science13551428Mistral Medium 3.5
LMArena Text: medicine and healthcare13491409Mistral Medium 3.5
LMArena Text: legal and government13451437Mistral Medium 3.5
LMArena Text: business and finance13291426Mistral Medium 3.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for Trinity Large Thinking and Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
Design Arena: websites11371142Mistral Medium 3.5
Design Arena: UI components10421076Mistral Medium 3.5
Design Arena: game dev10781079Mistral Medium 3.5
Design Arena: data viz10981120Mistral Medium 3.5
Design Arena: 3D10811060Trinity Large Thinking
Design Arena: all code categories11171121Mistral Medium 3.5
Design Arena: SVG1016949Trinity Large Thinking
Design Arena: ASCII art10411062Mistral Medium 3.5
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 Mistral Medium 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
SAGE (Vals AI)Not measured37.6%Only Mistral Medium 3.5 measured
MedScribe (Vals AI)Not measured67.7%Only Mistral Medium 3.5 measured
Vals Multimodal IndexNot measured34.8%Only Mistral Medium 3.5 measured
IFBench56.3%68.8%Mistral Medium 3.5
Long context reasoning (AA-LCR)38.0%69.3%Mistral Medium 3.5
DTBenchNot measured75.5%Only Mistral Medium 3.5 measured
LMCANot measured26.1%Only Mistral Medium 3.5 measured
Surface Evolver Bench15.6%26.9%Mistral Medium 3.5
LMArena Text13401421Mistral Medium 3.5
LMArena Text: instruction following13261415Mistral Medium 3.5
LMArena Text: creative writing13081374Mistral Medium 3.5
LMArena VisionNot measured1222Only Mistral Medium 3.5 measured
LMArena Text: English13561433Mistral Medium 3.5
LMArena Text: non-English13251404Mistral Medium 3.5
LMArena Text: Chinese13711444Mistral Medium 3.5
LMArena Text: German13561453Mistral Medium 3.5
LMArena Text: Japanese1312Not measuredOnly Trinity Large Thinking measured
LMArena Text: Korean13061384Mistral Medium 3.5
LMArena Text: French13661448Mistral Medium 3.5
LMArena Text: Spanish13271411Mistral Medium 3.5
LMArena Text: Russian13381395Mistral Medium 3.5
LMArena Text: Polish13121430Mistral Medium 3.5
LMArena Text: multi-turn13201423Mistral Medium 3.5
LMArena Text: longer queries13181415Mistral Medium 3.5
LMArena Text: writing and language13171384Mistral Medium 3.5
LMArena Text: entertainment and media12961382Mistral Medium 3.5
LMArena Vision: EnglishNot measured1240Only Mistral Medium 3.5 measured
LMArena Vision: ChineseNot measured1244Only Mistral Medium 3.5 measured
LMArena Vision: reading textNot measured1233Only Mistral Medium 3.5 measured
LMArena Vision: humorNot measured1216Only Mistral Medium 3.5 measured
LMArena Vision: creative writingNot measured1198Only Mistral Medium 3.5 measured
Neither model has a result on the other 15 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 3.5
BenchmarkTrinity Large ThinkingMistral Medium 3.5Leader
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 3.5$0.0053Fits 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 3.5$0.098Fits 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 3.5$0.375Fits 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 Mistral Medium 3.5
SpecificationArcee AI Trinity Large ThinkingMistral Medium 3.5
MakerArcee AIMistral AI
WeightsOpen weightsNo public weights listed
ReleasedApril 1, 2026April 30, 2026
Context window262K262K
Input $/Mtok$0.25$1.50
Output $/Mtok$0.80$7.50
Coding indexNot benchmarked25.5
Independent measurements behind it13
Cost per solved taskNot computable$6.02
SourceOpenRouter listingOpenRouter listing

Questions

Is Trinity Large Thinking better than Mistral Medium 3.5 for coding?

Only Mistral Medium 3.5 has a coding score, 25.5 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 9.4x cheaper ($0.80 vs $7.50 per million) and work measured by SciCode or GPQA Diamond (Artificial Analysis run), where it leads.

Which is cheaper, Trinity Large Thinking or Mistral Medium 3.5?

Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, Trinity Large Thinking or Mistral Medium 3.5?

Neither: both take 262K tokens.

How many benchmarks is the Trinity Large Thinking vs Mistral Medium 3.5 comparison based on?

51 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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