Trinity Large Thinking vs MiniMax M2
MiniMax M2 leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 18.6 to 10.8, and Trinity Large Thinking costs 1.3x less per output token ($0.80 vs $1.02 per million). Updated October 22, 2026; 42 benchmarks have a result for both.
Trinity Large Thinking or MiniMax M2: 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 1.3x cheaper ($0.80 vs $1.02 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 42
- Trinity Large Thinking only
- 10
- MiniMax M2 only
- 8
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2
MiniMax M2 leads, 18.6 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Trinity Large Thinking has a AA Agentic Index result, 1.0.
Directional only
Web design
Design Arena: websites
MiniMax M2
MiniMax M2 leads, 1143 against 1137.
Same board, both measured
Long documents
Listed context window
Trinity Large Thinking
Trinity Large Thinking takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, MiniMax M2 $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2 has the number, $0.702.
Directional only
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, MiniMax M2 not benchmarked.
- Widest category
- Intelligence, 7.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Trinity Large Thinking 38.0% against MiniMax M2 64.3%, 26.3 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against MiniMax M2 $1.02 per million output tokens, 1.3x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, MiniMax M2 205K.
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.
| Category | Trinity Large Thinking | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 3 | ||
| Math | 1 | 1 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 7 | ||
| General | 9 | 8 | ||
| All shared boards | 14 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LMArena WebDev | 1238 | 1297 | MiniMax M2 |
| AA Coding Index | 25.8 | Not measured | Only Trinity Large Thinking measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 82.6% | Only MiniMax M2 measured |
| SciCode | 40.6% | Not measured | Only Trinity Large Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 25.8% | MiniMax M2 |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| LMArena Text: coding | 1357 | 1371 | MiniMax M2 |
| LMArena Text: software and IT | 1362 | 1373 | MiniMax M2 |
| LMArena WebDev: React | 1231 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: HTML | 1240 | 1306 | MiniMax M2 |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 18.6 | MiniMax M2 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Trinity Large Thinking measured |
| τ²-Bench Telecom | 90.1% | 86.8% | Trinity Large Thinking |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 75.2% | 77.7% | MiniMax M2 |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 13.7% | Trinity Large Thinking |
| LMArena Text: hard prompts | 1342 | 1357 | MiniMax M2 |
| LMArena Text: expert | 1359 | 1338 | Trinity Large Thinking |
| LMArena Text: hard prompts in English | 1356 | 1376 | MiniMax M2 |
| Kagi LLM Benchmark | Not measured | 57.8% | Only MiniMax M2 measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 78.3 | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 78.3% | Only MiniMax M2 measured |
| LMArena Text: math | 1365 | 1351 | Trinity Large Thinking |
| LMArena Text: mathematical work | 1350 | 1368 | MiniMax M2 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 57.9% | Not measured | Only Trinity Large Thinking measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only Trinity Large Thinking measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only MiniMax M2 measured |
| LMArena Text: science | 1355 | 1348 | Trinity Large Thinking |
| LMArena Text: medicine and healthcare | 1349 | 1367 | MiniMax M2 |
| LMArena Text: legal and government | 1345 | 1349 | MiniMax M2 |
| LMArena Text: business and finance | 1329 | 1336 | MiniMax M2 |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | 1143 | MiniMax M2 |
| Design Arena: UI components | 1042 | 1137 | MiniMax M2 |
| Design Arena: game dev | 1078 | 1118 | MiniMax M2 |
| Design Arena: data viz | 1098 | 1140 | MiniMax M2 |
| Design Arena: 3D | 1081 | 1098 | MiniMax M2 |
| Design Arena: all code categories | 1117 | 1135 | MiniMax M2 |
| Design Arena: SVG | 1016 | 1100 | MiniMax M2 |
| Design Arena: ASCII art | 1041 | Not measured | Only Trinity Large Thinking measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index98
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| IFBench | 56.3% | 72.3% | MiniMax M2 |
| Long context reasoning (AA-LCR) | 38.0% | 64.3% | MiniMax M2 |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1340 | Trinity Large Thinking |
| LMArena Text: instruction following | 1326 | 1329 | MiniMax M2 |
| LMArena Text: creative writing | 1308 | 1286 | Trinity Large Thinking |
| LMArena Text: English | 1356 | 1364 | MiniMax M2 |
| LMArena Text: non-English | 1325 | 1313 | Trinity Large Thinking |
| LMArena Text: Chinese | 1371 | 1366 | Trinity Large Thinking |
| LMArena Text: German | 1356 | 1357 | MiniMax M2 |
| LMArena Text: Japanese | 1312 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: Korean | 1306 | Not measured | Only Trinity Large Thinking measured |
| LMArena Text: French | 1366 | 1336 | Trinity Large Thinking |
| LMArena Text: Spanish | 1327 | 1326 | Trinity Large Thinking |
| LMArena Text: Russian | 1338 | 1330 | Trinity Large Thinking |
| LMArena Text: Polish | 1312 | 1312 | Trinity Large Thinking |
| LMArena Text: multi-turn | 1320 | 1361 | MiniMax M2 |
| LMArena Text: longer queries | 1318 | 1331 | MiniMax M2 |
| LMArena Text: writing and language | 1317 | 1295 | Trinity Large Thinking |
| LMArena Text: entertainment and media | 1296 | 1298 | MiniMax M2 |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | MiniMax M2 | Leader |
|---|---|---|---|
| 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 costs less
Repository review
50K input + 3K output tokens
Trinity Large Thinking costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Arcee AI Trinity Large Thinking | MiniMax M2 |
|---|---|---|
| Maker | Arcee AI | MiniMax |
| Weights | Open weights | Open weights |
| Released | April 1, 2026 | October 23, 2025 |
| Context window | 262K | 205K |
| Input $/Mtok | $0.25 | $0.255 |
| Output $/Mtok | $0.80 | $1.02 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than MiniMax M2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Trinity Large Thinking for output tokens 1.3x cheaper ($0.80 vs $1.02 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, Trinity Large Thinking or MiniMax M2?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, Trinity Large Thinking or MiniMax M2?
Trinity Large Thinking, with 262K tokens against 205K.
How many benchmarks is the Trinity Large Thinking vs MiniMax M2 comparison based on?
42 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and MiniMax M2's on 1. Where only one model has a result, the page says so instead of naming a winner.