Trinity Large Thinking vs Gemini 2.5 Pro
Gemini 2.5 Pro leads Trinity Large Thinking on the Artificial Analysis Intelligence Index, 16.1 to 10.8, and Trinity Large Thinking costs 12.5x less per output token ($0.80 vs $10.00 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
Trinity Large Thinking or Gemini 2.5 Pro: which to pick
Only Gemini 2.5 Pro has a coding score, 19.1 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 12.5x cheaper ($0.80 vs $10.00 per million) and work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
- Shared results
- 48
- Trinity Large Thinking only
- 4
- Gemini 2.5 Pro only
- 42
- 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
Only Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 16.1 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 1.6 against 1.0.
Same board, both measured
Web design
Design Arena: websites
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 1167 against 1137.
Same board, both measured
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Trinity Large Thinking
Trinity Large Thinking $0.0006, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only
At a glance
- Coding index
- Trinity Large Thinking not benchmarked, Gemini 2.5 Pro 19.1 from 3 benchmarks.
- Widest category
- General, 21.6 points apart on average over 19 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Trinity Large Thinking 90.1% against Gemini 2.5 Pro 54.1%, 36.0 points apart on a 0 to 100 scale.
- Output price
- Trinity Large Thinking $0.80 against Gemini 2.5 Pro $10.00 per million output tokens, 12.5x apart.
- Context window
- Trinity Large Thinking takes 262K tokens, Gemini 2.5 Pro 1.05M.
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 | Gemini 2.5 Pro | Ties |
|---|---|---|---|---|
| Coding | 2 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 6 | ||
| Web design | 1 | 5 | ||
| General | 1 | 18 | ||
| All shared boards | 5 | 43 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index25
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 53.6% | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1238 | 1226 | Trinity Large Thinking |
| AA Coding Index | 25.8 | 33.3 | Gemini 2.5 Pro |
| SWE-bench Verified (Vals AI) | Not measured | 54.4% | Only Gemini 2.5 Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 0.4% | Only Gemini 2.5 Pro measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.1% | Only Gemini 2.5 Pro measured |
| SciCode | 40.6% | 46.3% | Gemini 2.5 Pro |
| Terminal-Bench Hard (Artificial Analysis run) | 22.7% | 26.5% | Gemini 2.5 Pro |
| Terminal-Bench | Not measured | 32.6% | Only Gemini 2.5 Pro measured |
| GSO-Bench | Not measured | 3.9% | Only Gemini 2.5 Pro measured |
| WeirdML | Not measured | 54.0% | Only Gemini 2.5 Pro measured |
| LMArena Text: coding | 1357 | 1452 | Gemini 2.5 Pro |
| LMArena Text: software and IT | 1362 | 1459 | Gemini 2.5 Pro |
| LMArena WebDev: React | 1231 | Not measured | Only Trinity Large Thinking measured |
| LMArena WebDev: HTML | 1240 | 1235 | Trinity Large Thinking |
| LMArena WebDev: from an image | Not measured | 1285 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 16.1 | Gemini 2.5 Pro |
| Epoch Capabilities Index | Not measured | 145.1 | Only Gemini 2.5 Pro measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 1.6 | Gemini 2.5 Pro |
| τ²-Bench Telecom | 90.1% | 54.1% | Trinity Large Thinking |
| GDPval | Not measured | 23.3% | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | Not measured | 41.5% | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | Not measured | $574 | Only Gemini 2.5 Pro measured |
| BrowseComp-Plus | Not measured | 29.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 75.2% | 84.4% | Gemini 2.5 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 22.5% | Gemini 2.5 Pro |
| GPQA Diamond (Epoch AI run) | Not measured | 85.3% | Only Gemini 2.5 Pro measured |
| ARC-AGI | Not measured | 41.0% | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts | 1342 | 1455 | Gemini 2.5 Pro |
| LMArena Text: expert | 1359 | 1452 | Gemini 2.5 Pro |
| LMArena Text: hard prompts in English | 1356 | 1453 | Gemini 2.5 Pro |
| LMArena Vision: diagrams | Not measured | 1274 | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | Not measured | 1281 | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | Not measured | 70.3% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 87.7 | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 87.7% | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | Not measured | 84.7% | Only Gemini 2.5 Pro measured |
| FrontierMath, tiers 1 to 3 | Not measured | 24.6% | Only Gemini 2.5 Pro measured |
| FrontierMath, tier 4 | Not measured | 0.0% | Only Gemini 2.5 Pro measured |
| LMArena Text: math | 1365 | 1450 | Gemini 2.5 Pro |
| LMArena Text: mathematical work | 1350 | 1454 | Gemini 2.5 Pro |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 57.9% | 63.9% | Gemini 2.5 Pro |
| CorpFin (Vals AI) | 54.7% | 60.8% | Gemini 2.5 Pro |
| MortgageTax (Vals AI) | Not measured | 68.9% | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | Not measured | 50.6% | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only Gemini 2.5 Pro measured |
| LMArena Text: science | 1355 | 1480 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1349 | 1476 | Gemini 2.5 Pro |
| LMArena Text: legal and government | 1345 | 1480 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1329 | 1439 | Gemini 2.5 Pro |
| FACTS Benchmark Suite | Not measured | 61.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1137 | 1167 | Gemini 2.5 Pro |
| Design Arena: UI components | 1042 | 1137 | Gemini 2.5 Pro |
| Design Arena: game dev | 1078 | 1111 | Gemini 2.5 Pro |
| Design Arena: data viz | 1098 | 1222 | Gemini 2.5 Pro |
| Design Arena: 3D | 1081 | 1080 | Trinity Large Thinking |
| Design Arena: all code categories | 1117 | 1152 | Gemini 2.5 Pro |
| Design Arena: SVG | 1016 | Not measured | Only Trinity Large Thinking measured |
| 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 index118
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.9% | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | Not measured | 73.5% | Only Gemini 2.5 Pro measured |
| IFBench | 56.3% | 48.7% | Trinity Large Thinking |
| Long context reasoning (AA-LCR) | 38.0% | 69.0% | Gemini 2.5 Pro |
| DTBench | Not measured | 82.4% | Only Gemini 2.5 Pro measured |
| LMCA | Not measured | 34.8% | Only Gemini 2.5 Pro measured |
| Creative writing (Lech Mazur) | Not measured | 83.8% | Only Gemini 2.5 Pro measured |
| Surface Evolver Bench | 15.6% | Not measured | Only Trinity Large Thinking measured |
| LMArena Text | 1340 | 1458 | Gemini 2.5 Pro |
| LMArena Text: instruction following | 1326 | 1437 | Gemini 2.5 Pro |
| LMArena Text: creative writing | 1308 | 1454 | Gemini 2.5 Pro |
| LMArena Vision | Not measured | 1263 | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1356 | 1458 | Gemini 2.5 Pro |
| LMArena Text: non-English | 1325 | 1452 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1371 | 1507 | Gemini 2.5 Pro |
| LMArena Text: German | 1356 | 1487 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1312 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Korean | 1306 | 1434 | Gemini 2.5 Pro |
| LMArena Text: French | 1366 | 1473 | Gemini 2.5 Pro |
| LMArena Text: Spanish | 1327 | 1474 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1338 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Polish | 1312 | 1479 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1320 | 1453 | Gemini 2.5 Pro |
| LMArena Text: longer queries | 1318 | 1449 | Gemini 2.5 Pro |
| LMArena Text: writing and language | 1317 | 1454 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1296 | 1441 | Gemini 2.5 Pro |
| LMArena Vision: English | Not measured | 1261 | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | Not measured | 1300 | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | Not measured | 1262 | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | Not measured | 1304 | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | Not measured | 1283 | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | Not measured | 1265 | Only Gemini 2.5 Pro measured |
| LMArena Document | Not measured | 1421 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Trinity Large Thinking | Gemini 2.5 Pro | 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 | Google Gemini 2.5 Pro |
|---|---|---|
| Maker | Arcee AI | |
| Weights | Open weights | No public weights listed |
| Released | April 1, 2026 | June 17, 2025 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.25 | $1.25 |
| Output $/Mtok | $0.80 | $10.00 |
| Coding index | Not benchmarked | 19.1 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Trinity Large Thinking better than Gemini 2.5 Pro for coding?
Only Gemini 2.5 Pro has a coding score, 19.1 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 12.5x cheaper ($0.80 vs $10.00 per million) and work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
Which is cheaper, Trinity Large Thinking or Gemini 2.5 Pro?
Trinity Large Thinking costs $0.25 per million input tokens and $0.80 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Cost per solved task is known for Gemini 2.5 Pro only.
Which has the bigger context window, Trinity Large Thinking or Gemini 2.5 Pro?
Gemini 2.5 Pro, with 1.05M tokens against 262K.
How many benchmarks is the Trinity Large Thinking vs Gemini 2.5 Pro comparison based on?
48 benchmarks have a result for both models as of October 22, 2026. Trinity Large Thinking's coding index rests on 1 independent benchmark and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.