Model comparison
GPT-5.4 nano vs Trinity Large Thinking
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 38.6 on the Noometry Index.
Last verified . 20 shared benchmarks.
Arcee AISummary
- They share 20 benchmarks with published results for both. GPT-5.4 nano scores higher in 8 categories and Trinity Large Thinking in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-5.4 nano leads 43.6 to 34.1.
- The biggest single-benchmark swing is SciCode: 46.9% for GPT-5.4 nano and 36.1% for Trinity Large Thinking.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.20 / $1.25 for GPT-5.4 nano.
- GPT-5.4 nano accepts more context: 400K tokens versus 262K.
- Trinity Large Thinking has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 nano | Trinity Large Thinking | |
|---|---|---|
| Provider | OpenAI | Arcee AI |
| Noometry Index | 41.9 | 38.6 |
| Released | 2026-03-17 | 2026-04-01 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 80K |
| Input $ / M tokens | $0.20 | $0.25 |
| Output $ / M tokens | $1.25 | $0.80 |
| Results tracked | 40 | 24 |
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Category by category
Coding GPT-5.4 nano leads
GPT-5.4 nano: 43.6 (#84), Trinity Large Thinking: 34.1 (#244)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| SciCode | 46.9% | 36.1% |
| LMArena Coding | 1405 | 1381 |
| LMArena WebDev | — | 1238 |
| WeirdML | 49.2% | — |
| ALE-Bench | 1,005 | — |
Reasoning GPT-5.4 nano leads
GPT-5.4 nano: 23.7 (#173), Trinity Large Thinking: 16.9 (#298)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| CritPt | 9.3% | 0.9% |
| LMArena Hard Prompts | 1381 | 1350 |
| ARC-AGI-2 | 5.7% | — |
| Kagi LLM Benchmark | 39.7% | — |
| NYT Connections (extended) | — | 16.5% |
| ARC-AGI-1 | 51.5% | — |
| Chess Puzzles | 30% | — |
| Thematic Generalization | — | 41.6% |
| Mystery Game Puzzles | 9% | — |
| DTBench | 80.3% | — |
| LMCA | 36.9% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 145.81 | — |
| ForecastBench | 57.3 | — |
Math GPT-5.4 nano leads
GPT-5.4 nano: 40.9 (#88), Trinity Large Thinking: 37.6 (#149)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1406 | 1366 |
| FrontierMath (Tiers 1-3) | 44.9% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 5% | — |
| FrontierMath (Feb 2025 set) | 25.9% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Too close to call
GPT-5.4 nano: 41.9 (#103), Trinity Large Thinking: 40.9 (#113)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | 3.1% | 6.9% |
| LMArena Expert | 1396 | 1360 |
| GPQA Diamond | 78.5% | — |
| SimpleQA Verified | 11.7% | — |
Multimodal Not comparable
GPT-5.4 nano: 36.7 (#78), Trinity Large Thinking: —
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Vision | 1196 | — |
Multilingual GPT-5.4 nano leads
GPT-5.4 nano: 48.6 (#140), Trinity Large Thinking: 46.2 (#160)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1359 | 1325 |
| LMArena Chinese | 1392 | 1373 |
| LMArena French | 1396 | 1374 |
| LMArena German | 1367 | 1356 |
| LMArena Japanese | 1343 | 1311 |
| LMArena Korean | 1320 | 1306 |
| LMArena Russian | 1363 | 1337 |
| LMArena Spanish | 1371 | 1357 |
Instruction Following GPT-5.4 nano leads
GPT-5.4 nano: 71.9 (#144), Trinity Large Thinking: 70.5 (#162)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1362 | 1334 |
Long Context Too close to call
GPT-5.4 nano: 41.6 (#137), Trinity Large Thinking: 41.3 (#144)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1366 | 1355 |
Writing & Preference GPT-5.4 nano leads
GPT-5.4 nano: 55.7 (#142), Trinity Large Thinking: 53.8 (#158)
| Benchmark | GPT-5.4 nano | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1372 | 1340 |
| LMArena Creative Writing | 1314 | 1320 |
| LMArena Multi-Turn | 1382 | 1342 |
Frequently asked questions
Is GPT-5.4 nano better than Trinity Large Thinking?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 38.6 on the Noometry Index.
Which is cheaper, GPT-5.4 nano 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-5.4 nano lists at $0.20 and $1.25.
Is GPT-5.4 nano or Trinity Large Thinking better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 nano does, with 400K tokens against 262K.
How many benchmarks do GPT-5.4 nano and Trinity Large Thinking share?
20 benchmarks have published results for both models. GPT-5.4 nano has 40 scored results on Noometry and Trinity Large Thinking has 24.