Model comparison
DeepSeek-V3.1 vs Trinity Large Thinking
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 38.6 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeek
Arcee AISummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Trinity Large Thinking in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 16.9.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Trinity Large Thinking accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.1 | Trinity Large Thinking | |
|---|---|---|
| Provider | DeepSeek | Arcee AI |
| Noometry Index | 42.8 | 38.6 |
| Released | 2025-08-21 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 8K | 80K |
| Input $ / M tokens | $0.25 | $0.25 |
| Output $ / M tokens | $0.95 | $0.80 |
| Results tracked | 27 | 24 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Trinity Large Thinking: 34.1 (#244)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Coding | 1417 | 1381 |
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Trinity Large Thinking: 16.9 (#298)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1350 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 41.6% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Trinity Large Thinking: 37.6 (#149)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1420 | 1366 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Trinity Large Thinking: 40.9 (#113)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 6.9% |
| LMArena Expert | 1405 | 1360 |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Trinity Large Thinking: 46.2 (#160)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1400 | 1325 |
| LMArena Chinese | 1469 | 1373 |
| LMArena French | 1447 | 1374 |
| LMArena German | 1411 | 1356 |
| LMArena Japanese | 1378 | 1311 |
| LMArena Korean | 1337 | 1306 |
| LMArena Russian | 1405 | 1337 |
| LMArena Spanish | 1431 | 1357 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Trinity Large Thinking: 70.5 (#162)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1400 | 1334 |
Long Context Trinity Large Thinking leads
DeepSeek-V3.1: 36.3 (#232), Trinity Large Thinking: 41.3 (#144)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1422 | 1355 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Trinity Large Thinking: 53.8 (#158)
| Benchmark | DeepSeek-V3.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1420 | 1340 |
| LMArena Creative Writing | 1401 | 1320 |
| LMArena Multi-Turn | 1408 | 1342 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Trinity Large Thinking?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 38.6 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1 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; DeepSeek-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or Trinity Large Thinking better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Trinity Large Thinking does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and Trinity Large Thinking share?
18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Trinity Large Thinking has 24.