# Llama 3.1-8B vs MiniMax-M2

> MiniMax-M2 is the stronger model overall, scoring 36.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 9.1× less per token, which makes it the better buy when MiniMax-M2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-minimax-m2
- Last updated: 2026-10-10
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and MiniMax-M2 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax-M2 leads 36.3 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.
- MiniMax-M2 accepts more context: 205K tokens versus 128K.

## Snapshot

| | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 22.8 | 36.9 |
| Rank | 347 | 201 |
| Context | 128K | 205K |
| Input $/M | $0.05 | $0.30 |
| Output $/M | $0.08 | $1.20 |
| Weights | Open | Open |

## Coding

- Llama 3.1-8B: 19.9 (#335)
- MiniMax-M2: 39.1 (#151)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Coding | 1195 | 1370 |
| SWE-bench Verified (bash only) | — | 61% |
| LMArena WebDev | — | 1297 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.3 (#123)
- MiniMax-M2: 24.9 (#102)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| Terminal-Bench | — | 30% |
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
| Vending-Bench 2 | — | 160.6 |

## Reasoning

- Llama 3.1-8B: 14.5 (#316)
- MiniMax-M2: 18.6 (#256)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1357 |
| Kagi LLM Benchmark | — | 57.8% |
| NYT Connections (extended) | — | 14.8% |
| CritPt | 0% | — |
| 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)
- MiniMax-M2: 34.6 (#158)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Math | 1179 | 1350 |
| 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 (#302)
- MiniMax-M2: 36.3 (#160)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Expert | 1144 | 1336 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |

## Multilingual

- Llama 3.1-8B: 34.2 (#244)
- MiniMax-M2: 45.6 (#165)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Non-English | 1148 | 1314 |
| LMArena Chinese | 1151 | 1367 |
| LMArena French | 1176 | 1336 |
| LMArena German | 1144 | 1366 |
| LMArena Russian | 1158 | 1332 |
| LMArena Spanish | 1169 | 1326 |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |

## Instruction Following

- Llama 3.1-8B: 59.1 (#253)
- MiniMax-M2: 70.4 (#160)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1329 |
| IFEval | 74.3% | — |

## Long Context

- Llama 3.1-8B: 35.8 (#233)
- MiniMax-M2: 40.5 (#148)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Longer Query | 1182 | 1332 |

## Writing & Preference

- Llama 3.1-8B: 29.9 (#285)
- MiniMax-M2: 53.3 (#157)

| Benchmark | Llama 3.1-8B | MiniMax-M2 |
|---|---|---|
| LMArena Text | 1187 | 1340 |
| LMArena Creative Writing | 1154 | 1288 |
| LMArena Multi-Turn | 1172 | 1362 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |

## FAQ

### Is Llama 3.1-8B better than MiniMax-M2?

MiniMax-M2 is the stronger model overall, scoring 36.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 9.1× less per token, which makes it the better buy when MiniMax-M2's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or MiniMax-M2?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiniMax-M2 lists at $0.30 and $1.20.

### Is Llama 3.1-8B or MiniMax-M2 better for coding?

MiniMax-M2 scores higher on coding benchmarks: 39.1 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2 does, with 205K tokens against 128K.

### How many benchmarks do Llama 3.1-8B and MiniMax-M2 share?

15 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiniMax-M2 has 21.