# Llama-3.3-70B-Instruct vs Trinity Large Thinking

> Trinity Large Thinking is the stronger model overall, scoring 38.1 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.5× less per token, which makes it the better buy when Trinity Large Thinking's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-trinity-large-thinking
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Trinity Large Thinking in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Trinity Large Thinking leads 34.9 to 14.3.
- The biggest single-benchmark swing is SciCode: 26% for Llama-3.3-70B-Instruct and 36.1% for Trinity Large Thinking.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.25 / $0.80 for Trinity Large Thinking.
- Trinity Large Thinking accepts more context: 262K tokens versus 128K.

## Snapshot

| | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| Provider | Meta | Arcee AI |
| Noometry Index | 30.3 | 38.1 |
| Rank | 283 | 180 |
| Context | 128K | 262K |
| Input $/M | $0.10 | $0.25 |
| Output $/M | $0.32 | $0.80 |
| Weights | Open | Open |

## Coding

- Llama-3.3-70B-Instruct: 30.6 (#284)
- Trinity Large Thinking: 33.8 (#238)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| SciCode | 26% | 36.1% |
| LMArena Coding | 1268 | 1381 |
| LMArena WebDev | — | 1238 |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.6 (#100)
- Trinity Large Thinking: —

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 13.7 (#325)
- Trinity Large Thinking: 16.3 (#298)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| CritPt | 0% | 0.9% |
| LMArena Hard Prompts | 1257 | 1350 |
| SimpleBench | 19.9% | — |
| NYT Connections (extended) | — | 16.5% |
| Thematic Generalization | — | 41.6% |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 14.3 (#292)
- Trinity Large Thinking: 34.9 (#149)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1268 | 1366 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.5 (#220)
- Trinity Large Thinking: 40.3 (#107)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | 4.1% | 6.9% |
| LMArena Expert | 1225 | 1359 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |

## Multilingual

- Llama-3.3-70B-Instruct: 40.1 (#215)
- Trinity Large Thinking: 46.4 (#155)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1236 | 1325 |
| LMArena Chinese | 1217 | 1372 |
| LMArena French | 1280 | 1374 |
| LMArena German | 1251 | 1356 |
| LMArena Japanese | 1149 | 1312 |
| LMArena Korean | 1142 | 1304 |
| LMArena Russian | 1252 | 1338 |
| LMArena Spanish | 1269 | 1356 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.3 (#152)
- Trinity Large Thinking: 70.7 (#157)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1242 | 1334 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#290)
- Trinity Large Thinking: 41.2 (#139)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1256 | 1355 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.8 (#203)
- Trinity Large Thinking: 54.0 (#152)

| Benchmark | Llama-3.3-70B-Instruct | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1274 | 1340 |
| LMArena Creative Writing | 1250 | 1321 |
| LMArena Multi-Turn | 1279 | 1342 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Trinity Large Thinking?

Trinity Large Thinking is the stronger model overall, scoring 38.1 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.5× less per token, which makes it the better buy when Trinity Large Thinking's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or Trinity Large Thinking?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Trinity Large Thinking lists at $0.25 and $0.80.

### Is Llama-3.3-70B-Instruct or Trinity Large Thinking better for coding?

Trinity Large Thinking scores higher on coding benchmarks: 33.8 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

Trinity Large Thinking does, with 262K tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and Trinity Large Thinking share?

20 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Trinity Large Thinking has 24.