# Llama 3.1-8B vs MiMo-V2-Pro

> MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 9.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-mimo-v2-pro
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and MiMo-V2-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiMo-V2-Pro leads 41.4 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 23.0 | 43.0 |
| Rank | 352 | 103 |
| Context | 128K | 1.05M |
| Input $/M | $0.05 | $0.43 |
| Output $/M | $0.08 | $0.87 |
| Weights | Open | Proprietary |

## Coding

- Llama 3.1-8B: 20.2 (#340)
- MiMo-V2-Pro: 43.8 (#83)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1195 | 1476 |
| LMArena WebDev | — | 1433 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 785.17 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.5 (#131)
- MiMo-V2-Pro: —

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |

## Reasoning

- Llama 3.1-8B: 14.9 (#321)
- MiMo-V2-Pro: 22.1 (#206)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1457 |
| NYT Connections (extended) | — | 25.8% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 45.9% |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |

## Math

- Llama 3.1-8B: 10.2 (#317)
- MiMo-V2-Pro: 39.5 (#102)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1179 | 1447 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |

## Knowledge

- Llama 3.1-8B: 8.0 (#307)
- MiMo-V2-Pro: 41.4 (#111)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1144 | 1478 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |

## Multilingual

- Llama 3.1-8B: 34.0 (#249)
- MiMo-V2-Pro: 52.7 (#81)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1148 | 1416 |
| LMArena Chinese | 1151 | 1456 |
| LMArena French | 1177 | 1469 |
| LMArena German | 1144 | 1417 |
| LMArena Japanese | 1061 | 1366 |
| LMArena Korean | 1053 | 1400 |
| LMArena Russian | 1158 | 1427 |
| LMArena Spanish | 1169 | 1457 |

## Instruction Following

- Llama 3.1-8B: 58.9 (#258)
- MiMo-V2-Pro: 76.0 (#49)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1159 | 1445 |
| IFEval | 74.3% | — |

## Long Context

- Llama 3.1-8B: 35.8 (#238)
- MiMo-V2-Pro: 41.5 (#138)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1182 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |

## Writing & Preference

- Llama 3.1-8B: 29.7 (#290)
- MiMo-V2-Pro: 62.8 (#70)

| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1187 | 1436 |
| LMArena Creative Writing | 1154 | 1415 |
| LMArena Multi-Turn | 1172 | 1456 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |

## FAQ

### Is Llama 3.1-8B better than MiMo-V2-Pro?

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 9.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or MiMo-V2-Pro?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.

### Is Llama 3.1-8B or MiMo-V2-Pro better for coding?

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2-Pro does, with 1.05M tokens against 128K.

### How many benchmarks do Llama 3.1-8B and MiMo-V2-Pro share?

17 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiMo-V2-Pro has 23.