# Llama-3.3-70B-Instruct vs Mercury

> Mercury is the stronger model overall, scoring 37.3 to 30.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-mercury
- Last updated: 2026-10-10
- Shared benchmarks: 8

## Summary

- They share 8 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 2 categories and Mercury in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Mercury leads 38.3 to 26.4.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 30.3 | 37.3 |
| Rank | 283 | 192 |
| Context | 128K | — |
| Input $/M | $0.10 | — |
| Output $/M | $0.32 | — |
| Weights | Open | Proprietary |

## Coding

- Llama-3.3-70B-Instruct: 30.6 (#284)
- Mercury: 38.1 (#164)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Coding | 1268 | 1323 |
| SciCode | 26% | — |
| 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)
- Mercury: —

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

## Reasoning

- Llama-3.3-70B-Instruct: 13.7 (#325)
- Mercury: 16.8 (#288)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1284 |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 21.6% |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 14.3 (#292)
- Mercury: —

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

## Knowledge

- Llama-3.3-70B-Instruct: 30.5 (#220)
- Mercury: —

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

## Multilingual

- Llama-3.3-70B-Instruct: 40.1 (#215)
- Mercury: 41.7 (#201)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Non-English | 1236 | 1259 |
| LMArena Chinese | 1217 | — |
| LMArena French | 1280 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1149 | — |
| LMArena Korean | 1142 | — |
| LMArena Russian | 1252 | — |
| LMArena Spanish | 1269 | — |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.3 (#152)
- Mercury: 65.3 (#220)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Instruction Following | 1242 | 1238 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#290)
- Mercury: 38.3 (#193)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Longer Query | 1256 | 1264 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.8 (#203)
- Mercury: 46.2 (#216)

| Benchmark | Llama-3.3-70B-Instruct | Mercury |
|---|---|---|
| LMArena Text | 1274 | 1282 |
| LMArena Creative Writing | 1250 | 1186 |
| LMArena Multi-Turn | 1279 | 1283 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Mercury?

Mercury is the stronger model overall, scoring 37.3 to 30.3 on the Noometry Index.

### Is Llama-3.3-70B-Instruct or Mercury better for coding?

Mercury scores higher on coding benchmarks: 38.1 versus 30.6 in the Noometry coding category.

### How many benchmarks do Llama-3.3-70B-Instruct and Mercury share?

8 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mercury has 9.