# Llama 3-8B vs MiniMax-M2.7

> MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 25.3 on the Noometry Index. Llama 3-8B costs 3.7× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-8b-vs-minimax-m2-7
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Llama 3-8B scores higher in 0 categories and MiniMax-M2.7 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax-M2.7 leads 37.1 to 7.8.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
- MiniMax-M2.7 accepts more context: 205K tokens versus 8K.

## Snapshot

| | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 25.3 | 37.4 |
| Rank | 338 | 188 |
| Context | 8K | 205K |
| Input $/M | $0.14 | $0.30 |
| Output $/M | $0.14 | $1.20 |
| Weights | Open | Open |

## Coding

- Llama 3-8B: 30.5 (#285)
- MiniMax-M2.7: 41.4 (#115)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Coding | 1152 | 1454 |
| LMArena WebDev | — | 1397 |
| SciCode | — | 47% |
| WeirdML | — | 37% |
| BigCodeBench Instruct | 31.9% | — |
| BigCodeBench Complete | 36.9% | — |
| ALE-Bench | — | 599.25 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |

## Agentic & Tool Use

- Llama 3-8B: —
- MiniMax-M2.7: 24.9 (#103)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | — | 45.1% |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |

## Reasoning

- Llama 3-8B: 13.8 (#321)
- MiniMax-M2.7: 19.0 (#248)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Hard Prompts | 1133 | 1422 |
| Epoch Capabilities Index | 116.33 | 145.84 |
| NYT Connections (extended) | — | 24.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 39.3% |
| DTBench | 43.9% | — |
| Adversarial NLI | 57.3% | — |
| ForecastBench | 58.6 | — |
| WinoGrande | 75.7% | — |

## Math

- Llama 3-8B: 8.3 (#317)
- MiniMax-M2.7: 24.9 (#254)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Math | 1151 | 1420 |
| OTIS Mock AIME 2024-2025 | 1.9% | — |
| ProofBench | — | 3% |
| MATH Level 5 | 6.1% | — |

## Knowledge

- Llama 3-8B: 7.8 (#303)
- MiniMax-M2.7: 37.1 (#151)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Expert | 1113 | 1443 |
| GPQA Diamond | 26.1% | — |
| Vectara Hallucination Rate | — | 12.9% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |

## Multilingual

- Llama 3-8B: 31.0 (#256)
- MiniMax-M2.7: 50.5 (#120)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 1098 | 1382 |
| LMArena Chinese | 1076 | 1439 |
| LMArena French | 1158 | 1422 |
| LMArena German | 1104 | 1398 |
| LMArena Japanese | 967 | 1261 |
| LMArena Korean | 1003 | 1313 |
| LMArena Russian | 1109 | 1383 |
| LMArena Spanish | 1172 | 1403 |

## Instruction Following

- Llama 3-8B: 58.7 (#255)
- MiniMax-M2.7: 74.2 (#101)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1127 | 1404 |

## Long Context

- Llama 3-8B: 34.2 (#246)
- MiniMax-M2.7: 43.3 (#94)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1128 | 1419 |

## Writing & Preference

- Llama 3-8B: 37.7 (#251)
- MiniMax-M2.7: 59.1 (#107)

| Benchmark | Llama 3-8B | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1166 | 1404 |
| LMArena Creative Writing | 1150 | 1354 |
| LMArena Multi-Turn | 1152 | 1413 |

## FAQ

### Is Llama 3-8B better than MiniMax-M2.7?

MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 25.3 on the Noometry Index. Llama 3-8B costs 3.7× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.

### Which is cheaper, Llama 3-8B or MiniMax-M2.7?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.

### Is Llama 3-8B or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2.7 does, with 205K tokens against 8K.

### How many benchmarks do Llama 3-8B and MiniMax-M2.7 share?

18 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and MiniMax-M2.7 has 30.