Model comparison

MiniMax M1 vs Trinity Large Thinking

MiniMax M1 is the stronger model overall, scoring 40.3 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.5× less per token, which makes it the better buy when MiniMax M1's lead doesn't matter for your workload.

Last verified . 17 shared benchmarks.

MiniMax M1 MiniMax

40.3

Rank #150 Confirmed

Trinity Large Thinking Arcee AI

38.6

Rank #185 Confirmed

Summary

  • They share 17 benchmarks with published results for both. MiniMax M1 scores higher in 3 categories and Trinity Large Thinking in 5 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where MiniMax M1 leads 26.9 to 16.9.
  • Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.55 / $2.20 for MiniMax M1.
  • MiniMax M1 accepts more context: 1M tokens versus 262K.

Side by side

MiniMax M1 and Trinity Large Thinking specifications
MiniMax M1Trinity Large Thinking
ProviderMiniMaxArcee AI
Noometry Index40.338.6
Released2025-06-132026-04-01
WeightsOpenOpen
Context window1M262K
Max output40K80K
Input $ / M tokens$0.55$0.25
Output $ / M tokens$2.20$0.80
Results tracked1824

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Category by category

Coding MiniMax M1 leads

MiniMax M1: 39.9 (#153), Trinity Large Thinking: 34.1 (#244)

Coding benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Coding13591381
LMArena WebDev—1238
SciCode—36.1%

Reasoning MiniMax M1 leads

MiniMax M1: 26.9 (#126), Trinity Large Thinking: 16.9 (#298)

Reasoning benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Hard Prompts13391350
NYT Connections (extended)—16.5%
CritPt—0.9%
Thematic Generalization—41.6%
Surface Evolver Bench—15.6%

Math Too close to call

MiniMax M1: 37.5 (#151), Trinity Large Thinking: 37.6 (#149)

Math benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Math13611366

Knowledge Trinity Large Thinking leads

MiniMax M1: 36.4 (#170), Trinity Large Thinking: 40.9 (#113)

Knowledge benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Expert13171360
Vectara Hallucination Rate—6.9%

Multilingual Too close to call

MiniMax M1: 45.8 (#163), Trinity Large Thinking: 46.2 (#160)

Multilingual benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Non-English13191325
LMArena Chinese13601373
LMArena French13701374
LMArena German13501356
LMArena Japanese12171311
LMArena Korean12661306
LMArena Russian13291337
LMArena Spanish13531357

Instruction Following Trinity Large Thinking leads

MiniMax M1: 69.3 (#174), Trinity Large Thinking: 70.5 (#162)

Instruction Following benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Instruction Following13121334

Long Context Too close to call

MiniMax M1: 41.4 (#141), Trinity Large Thinking: 41.3 (#144)

Long Context benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Longer Query13261355
Fiction.LiveBench69.4%—

Writing & Preference Too close to call

MiniMax M1: 53.1 (#161), Trinity Large Thinking: 53.8 (#158)

Writing & Preference benchmarks
BenchmarkMiniMax M1Trinity Large Thinking
LMArena Text13431340
LMArena Creative Writing12981320
LMArena Multi-Turn13351342

Frequently asked questions

Is MiniMax M1 better than Trinity Large Thinking?

MiniMax M1 is the stronger model overall, scoring 40.3 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.5× less per token, which makes it the better buy when MiniMax M1's lead doesn't matter for your workload.

Which is cheaper, MiniMax M1 or Trinity Large Thinking?

Trinity Large Thinking is cheaper. It lists at $0.25 per million input tokens and $0.80 per million output tokens; MiniMax M1 lists at $0.55 and $2.20.

Is MiniMax M1 or Trinity Large Thinking better for coding?

MiniMax M1 scores higher on coding benchmarks: 39.9 versus 34.1 in the Noometry coding category.

Which has the bigger context window?

MiniMax M1 does, with 1M tokens against 262K.

How many benchmarks do MiniMax M1 and Trinity Large Thinking share?

17 benchmarks have published results for both models. MiniMax M1 has 18 scored results on Noometry and Trinity Large Thinking has 24.

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