Model comparison
DeepSeek-V3.2-Speciale vs Trinity Large Thinking
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.2× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
DeepSeek
Arcee AISummary
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 16.9.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- Trinity Large Thinking accepts more context: 262K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | Trinity Large Thinking | |
|---|---|---|
| Provider | DeepSeek | Arcee AI |
| Noometry Index | 39.7 | 38.6 |
| Released | 2025-12-01 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 128K | 80K |
| Input $ / M tokens | $0.58 | $0.25 |
| Output $ / M tokens | $1.68 | $0.80 |
| Results tracked | 3 | 24 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Trinity Large Thinking: 34.1 (#244)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1381 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Trinity Large Thinking: 16.9 (#298)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 41.6% |
| LMArena Hard Prompts | — | 1350 |
| Surface Evolver Bench | — | 15.6% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Trinity Large Thinking: 37.6 (#149)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena Math | — | 1366 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Trinity Large Thinking: 40.9 (#113)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | — | 6.9% |
| LMArena Expert | — | 1360 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Trinity Large Thinking: 46.2 (#160)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | — | 1325 |
| LMArena Chinese | — | 1373 |
| LMArena French | — | 1374 |
| LMArena German | — | 1356 |
| LMArena Japanese | — | 1311 |
| LMArena Korean | — | 1306 |
| LMArena Russian | — | 1337 |
| LMArena Spanish | — | 1357 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Trinity Large Thinking: 70.5 (#162)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | — | 1334 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Trinity Large Thinking: 41.3 (#144)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | — | 1355 |
Writing & Preference Trinity Large Thinking leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Trinity Large Thinking: 53.8 (#158)
| Benchmark | DeepSeek-V3.2-Speciale | Trinity Large Thinking |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1320 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1342 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Trinity Large Thinking?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.2× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale 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-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or Trinity Large Thinking better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Trinity Large Thinking does, with 262K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Trinity Large Thinking share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Trinity Large Thinking has 24.