Model comparison
GPT-4.1 nano vs Trinity Large Thinking
Trinity Large Thinking is the stronger model overall, scoring 38.6 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.2× less per token, which makes it the better buy when Trinity Large Thinking's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Arcee AISummary
- They share 16 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and Trinity Large Thinking in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Trinity Large Thinking leads 40.9 to 21.8.
- The biggest single-benchmark swing is SciCode: 25.9% for GPT-4.1 nano and 36.1% for Trinity Large Thinking.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $0.80 for Trinity Large Thinking.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 262K.
- Trinity Large Thinking has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 nano | Trinity Large Thinking | |
|---|---|---|
| Provider | OpenAI | Arcee AI |
| Noometry Index | 27.9 | 38.6 |
| Released | 2025-04-14 | 2026-04-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 33K | 80K |
| Input $ / M tokens | $0.10 | $0.25 |
| Output $ / M tokens | $0.40 | $0.80 |
| Results tracked | 38 | 24 |
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Category by category
Coding Trinity Large Thinking leads
GPT-4.1 nano: 24.1 (#330), Trinity Large Thinking: 34.1 (#244)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| SciCode | 25.9% | 36.1% |
| LMArena Coding | 1306 | 1381 |
| Aider Polyglot | 8.9% | — |
| LMArena WebDev | — | 1238 |
| WeirdML | 19% | — |
Agentic & Tool Use Not comparable
GPT-4.1 nano: 26.5 (#104), Trinity Large Thinking: —
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| Berkeley Function Calling Leaderboard | 33% | — |
Reasoning Trinity Large Thinking leads
GPT-4.1 nano: 8.5 (#349), Trinity Large Thinking: 16.9 (#298)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| CritPt | 0% | 0.9% |
| LMArena Hard Prompts | 1286 | 1350 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 33.3% | — |
| NYT Connections (extended) | — | 16.5% |
| ARC-AGI-1 | 0% | — |
| Thematic Generalization | — | 41.6% |
| DTBench | 52.5% | — |
| LMCA | 5.5% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 129.62 | — |
Math Trinity Large Thinking leads
GPT-4.1 nano: 26.9 (#252), Trinity Large Thinking: 37.6 (#149)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1274 | 1366 |
| OTIS Mock AIME 2024-2025 | 28.9% | — |
| Omni-MATH | 36.7% | — |
| MATH Level 5 | 70% | — |
| FrontierMath (Feb 2025 set) | 1% | — |
Knowledge Trinity Large Thinking leads
GPT-4.1 nano: 21.8 (#273), Trinity Large Thinking: 40.9 (#113)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Expert | 1272 | 1360 |
| GPQA Diamond | 48.9% | — |
| SimpleQA Verified | 6% | — |
| MMLU-Pro | 55% | — |
| Vectara Hallucination Rate | — | 6.9% |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
GPT-4.1 nano: 29.2 (#113), Trinity Large Thinking: —
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Vision | 1063 | — |
Multilingual Trinity Large Thinking leads
GPT-4.1 nano: 41.6 (#205), Trinity Large Thinking: 46.2 (#160)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1260 | 1325 |
| LMArena Chinese | 1270 | 1373 |
| LMArena German | 1288 | 1356 |
| LMArena Japanese | 1198 | 1311 |
| LMArena Russian | 1261 | 1337 |
| LMArena French | — | 1374 |
| LMArena Korean | — | 1306 |
| LMArena Spanish | — | 1357 |
Instruction Following Trinity Large Thinking leads
GPT-4.1 nano: 67.8 (#193), Trinity Large Thinking: 70.5 (#162)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1267 | 1334 |
| IFEval | 84.3% | — |
Long Context Trinity Large Thinking leads
GPT-4.1 nano: 23.7 (#296), Trinity Large Thinking: 41.3 (#144)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1283 | 1355 |
| Fiction.LiveBench | 25% | — |
Writing & Preference Trinity Large Thinking leads
GPT-4.1 nano: 40.5 (#243), Trinity Large Thinking: 53.8 (#158)
| Benchmark | GPT-4.1 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1285 | 1340 |
| LMArena Creative Writing | 1260 | 1320 |
| LMArena Multi-Turn | 1277 | 1342 |
| EQ-Bench Creative Writing | 946 | — |
| WildBench | 81.2% | — |
Frequently asked questions
Is GPT-4.1 nano better than Trinity Large Thinking?
Trinity Large Thinking is the stronger model overall, scoring 38.6 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.2× less per token, which makes it the better buy when Trinity Large Thinking's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or Trinity Large Thinking?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Trinity Large Thinking lists at $0.25 and $0.80.
Is GPT-4.1 nano or Trinity Large Thinking better for coding?
Trinity Large Thinking scores higher on coding benchmarks: 34.1 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 262K.
How many benchmarks do GPT-4.1 nano and Trinity Large Thinking share?
16 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Trinity Large Thinking has 24.