Model comparison
GPT-5.6 Luna vs Trinity Large Thinking
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 38.1 on the Noometry Index.
Last verified . 22 shared benchmarks.
Arcee AISummary
- They share 22 benchmarks with published results for both. GPT-5.6 Luna scores higher in 8 categories and Trinity Large Thinking in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.5 to 34.9.
- The biggest single-benchmark swing is NYT Connections (extended): 69.4% for GPT-5.6 Luna and 16.5% for Trinity Large Thinking.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 262K.
- Trinity Large Thinking has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Trinity Large Thinking | |
|---|---|---|
| Provider | OpenAI | Arcee AI |
| Noometry Index | 54.7 | 38.1 |
| Released | 2026-07-09 | 2026-04-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 80K |
| Input $ / M tokens | $0.20 | $0.25 |
| Output $ / M tokens | $1.20 | $0.80 |
| Results tracked | 51 | 24 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 53.9 (#25), Trinity Large Thinking: 33.8 (#238)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | 1517 | 1238 |
| SciCode | 53.6% | 36.1% |
| LMArena Coding | 1466 | 1381 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| WeirdML | 60.9% | — |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 35.3 (#37), Trinity Large Thinking: —
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.5 (#38), Trinity Large Thinking: 16.3 (#298)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| NYT Connections (extended) | 69.4% | 16.5% |
| CritPt | 20.6% | 0.9% |
| LMArena Hard Prompts | 1452 | 1350 |
| Surface Evolver Bench | 61.9% | 15.6% |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| ARC-AGI-1 | 88% | — |
| Chess Puzzles | 40% | — |
| Thematic Generalization | — | 41.6% |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Epoch Capabilities Index | 156.32 | — |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.5 (#12), Trinity Large Thinking: 34.9 (#149)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1455 | 1366 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 60% | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.6 (#31), Trinity Large Thinking: 40.3 (#107)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Expert | 1479 | 1359 |
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 6.9% |
Multimodal Not comparable
GPT-5.6 Luna: 43.0 (#22), Trinity Large Thinking: —
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Vision | 1259 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual GPT-5.6 Luna leads
GPT-5.6 Luna: 53.0 (#73), Trinity Large Thinking: 46.4 (#155)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1418 | 1325 |
| LMArena Chinese | 1469 | 1372 |
| LMArena French | 1457 | 1374 |
| LMArena German | 1459 | 1356 |
| LMArena Japanese | 1416 | 1312 |
| LMArena Korean | 1414 | 1304 |
| LMArena Russian | 1428 | 1338 |
| LMArena Spanish | 1445 | 1356 |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 76.0 (#48), Trinity Large Thinking: 70.7 (#157)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1440 | 1334 |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#78), Trinity Large Thinking: 41.2 (#139)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1437 | 1355 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.3 (#27), Trinity Large Thinking: 54.0 (#152)
| Benchmark | GPT-5.6 Luna | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1432 | 1340 |
| LMArena Creative Writing | 1397 | 1321 |
| LMArena Multi-Turn | 1433 | 1342 |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Trinity Large Thinking?
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 38.1 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna 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; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Trinity Large Thinking better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.6 Luna and Trinity Large Thinking share?
22 benchmarks have published results for both models. GPT-5.6 Luna has 51 scored results on Noometry and Trinity Large Thinking has 24.