# Llama 3.1-8B vs Mercury 2

> Mercury 2 is the stronger model overall, scoring 38.8 to 22.8 on the Noometry Index. Llama 3.1-8B costs 6.5× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-mercury-2
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and Mercury 2 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Mercury 2 leads 35.6 to 8.0.
- The biggest single-benchmark swing is WeirdML: 1.7% for Llama 3.1-8B and 43.2% for Mercury 2.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.25 / $0.75 for Mercury 2.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 22.8 | 38.8 |
| Rank | 347 | 167 |
| Context | 128K | 128K |
| Input $/M | $0.05 | $0.25 |
| Output $/M | $0.08 | $0.75 |
| Weights | Open | Proprietary |

## Coding

- Llama 3.1-8B: 19.9 (#335)
- Mercury 2: 33.2 (#250)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| SciCode | 13.2% | 38.7% |
| WeirdML | 1.7% | 43.2% |
| LMArena Coding | 1195 | 1391 |
| LMArena WebDev | — | 1170 |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 785.58 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.3 (#123)
- Mercury 2: —

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |

## Reasoning

- Llama 3.1-8B: 14.5 (#316)
- Mercury 2: 23.0 (#170)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| CritPt | 0% | 0.8% |
| LMArena Hard Prompts | 1175 | 1362 |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.48 | — |
| PIQA | 81.2% | — |

## Math

- Llama 3.1-8B: 9.5 (#311)
- Mercury 2: —

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| LMArena Math | 1179 | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |

## Knowledge

- Llama 3.1-8B: 8.0 (#302)
- Mercury 2: 35.6 (#170)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Expert | 1144 | 1358 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| Vectara Hallucination Rate | — | 12.3% |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |

## Multilingual

- Llama 3.1-8B: 34.2 (#244)
- Mercury 2: 46.8 (#153)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1148 | 1331 |
| LMArena Chinese | 1151 | 1416 |
| LMArena Russian | 1158 | 1303 |
| LMArena French | 1176 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Spanish | 1169 | — |

## Instruction Following

- Llama 3.1-8B: 59.1 (#253)
- Mercury 2: 70.4 (#161)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1328 |
| IFEval | 74.3% | — |

## Long Context

- Llama 3.1-8B: 35.8 (#233)
- Mercury 2: 40.4 (#149)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1182 | 1329 |

## Writing & Preference

- Llama 3.1-8B: 29.9 (#285)
- Mercury 2: 54.0 (#151)

| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Text | 1187 | 1355 |
| LMArena Creative Writing | 1154 | 1289 |
| LMArena Multi-Turn | 1172 | 1358 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |

## FAQ

### Is Llama 3.1-8B better than Mercury 2?

Mercury 2 is the stronger model overall, scoring 38.8 to 22.8 on the Noometry Index. Llama 3.1-8B costs 6.5× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or Mercury 2?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Mercury 2 lists at $0.25 and $0.75.

### Is Llama 3.1-8B or Mercury 2 better for coding?

Mercury 2 scores higher on coding benchmarks: 33.2 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Both accept 128K tokens.

### How many benchmarks do Llama 3.1-8B and Mercury 2 share?

14 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Mercury 2 has 17.