# Llama 3.1-8B vs Mixtral 8x7B

> Mixtral 8x7B is the stronger model overall, scoring 27.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when Mixtral 8x7B's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-mixtral-8x7b
- Last updated: 2026-10-12
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. Llama 3.1-8B scores higher in 3 categories and Mixtral 8x7B in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Mixtral 8x7B leads 32.8 to 20.2.
- The biggest single-benchmark swing is IFEval: 74.3% for Llama 3.1-8B and 57.5% for Mixtral 8x7B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- Llama 3.1-8B accepts more context: 128K tokens versus 32K.

## Snapshot

| | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 23.0 | 27.1 |
| Rank | 352 | 334 |
| Context | 128K | 32K |
| Input $/M | $0.05 | $0.70 |
| Output $/M | $0.08 | $0.70 |
| Weights | Open | Open |

## Coding

- Llama 3.1-8B: 20.2 (#340)
- Mixtral 8x7B: 32.8 (#269)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1195 | 1126 |
| HumanEval+ | 62.8% | 39.6% |
| MBPP+ | 55.6% | 49.7% |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.5 (#131)
- Mixtral 8x7B: —

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |

## Reasoning

- Llama 3.1-8B: 14.9 (#321)
- Mixtral 8x7B: 18.2 (#285)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1115 |
| DTBench | 50.9% | 49.6% |
| Epoch Capabilities Index | 116.57 | 118.47 |
| PIQA | 81.2% | 83.6% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| LMCA | 5.4% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| WinoGrande | — | 77.2% |

## Math

- Llama 3.1-8B: 10.2 (#317)
- Mixtral 8x7B: 18.8 (#289)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | 13.7% | 10.5% |
| LMArena Math | 1179 | 1147 |
| MATH Level 5 | 22.9% | 10% |
| GSM8K | 82.4% | 74.4% |
| OTIS Mock AIME 2024-2025 | 1.7% | — |

## Knowledge

- Llama 3.1-8B: 8.0 (#307)
- Mixtral 8x7B: 11.0 (#301)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 27% | 30.6% |
| MMLU-Pro | 40.6% | 33.5% |
| GPQA (HELM) | 24.7% | 29.6% |
| LMArena Expert | 1144 | 1088 |
| MMLU | 56.1% | 70.6% |
| ARC (AI2) Challenge | — | 87.3% |
| BoolQ | 82.8% | — |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |

## Multilingual

- Llama 3.1-8B: 34.0 (#249)
- Mixtral 8x7B: 29.6 (#266)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1148 | 1077 |
| LMArena Chinese | 1151 | 1055 |
| LMArena French | 1177 | 1166 |
| LMArena German | 1144 | 1114 |
| LMArena Japanese | 1061 | 931 |
| LMArena Korean | 1053 | 968 |
| LMArena Russian | 1158 | 1090 |
| LMArena Spanish | 1169 | 1111 |

## Instruction Following

- Llama 3.1-8B: 58.9 (#258)
- Mixtral 8x7B: 51.0 (#297)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| IFEval | 74.3% | 57.5% |
| LMArena Instruction Following | 1159 | 1109 |

## Long Context

- Llama 3.1-8B: 35.8 (#238)
- Mixtral 8x7B: 33.4 (#260)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1182 | 1103 |

## Writing & Preference

- Llama 3.1-8B: 29.7 (#290)
- Mixtral 8x7B: 34.2 (#270)

| Benchmark | Llama 3.1-8B | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1187 | 1132 |
| LMArena Creative Writing | 1154 | 1109 |
| WildBench | 68.7% | 67.3% |
| LMArena Multi-Turn | 1172 | 1115 |
| EQ-Bench Creative Writing | 713 | — |

## FAQ

### Is Llama 3.1-8B better than Mixtral 8x7B?

Mixtral 8x7B is the stronger model overall, scoring 27.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when Mixtral 8x7B's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or Mixtral 8x7B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

### Is Llama 3.1-8B or Mixtral 8x7B better for coding?

Mixtral 8x7B scores higher on coding benchmarks: 32.8 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Llama 3.1-8B does, with 128K tokens against 32K.

### How many benchmarks do Llama 3.1-8B and Mixtral 8x7B share?

31 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Mixtral 8x7B has 38.