# Llama 3.2 1B vs MiniMax-M2.7

> MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 20.1 on the Noometry Index. Llama 3.2 1B costs 7.4× 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-3-2-1b-vs-minimax-m2-7
- Last updated: 2026-10-12
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and MiniMax-M2.7 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiniMax-M2.7 leads 58.9 to 21.3.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
- MiniMax-M2.7 accepts more context: 205K tokens versus 60K.

## Snapshot

| | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 20.1 | 37.7 |
| Rank | 354 | 196 |
| Context | 60K | 205K |
| Input $/M | $0.027 | $0.30 |
| Output $/M | $0.20 | $1.20 |
| Weights | Open | Open |

## Coding

- Llama 3.2 1B: 21.1 (#338)
- MiniMax-M2.7: 41.8 (#120)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Coding | 1070 | 1454 |
| LMArena WebDev | — | 1398 |
| SciCode | — | 47% |
| WeirdML | — | 37% |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| ALE-Bench | — | 599.25 |

## Agentic & Tool Use

- Llama 3.2 1B: 14.6 (#150)
- MiniMax-M2.7: 25.1 (#111)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | — | 45.1% |
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |

## Reasoning

- Llama 3.2 1B: 16.2 (#308)
- MiniMax-M2.7: 19.7 (#253)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1422 |
| Epoch Capabilities Index | 101.99 | 145.85 |
| NYT Connections (extended) | — | 24.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 39.3% |

## Math

- Llama 3.2 1B: 10.4 (#313)
- MiniMax-M2.7: 25.9 (#263)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Math | 1086 | 1420 |
| OTIS Mock AIME 2024-2025 | 0.6% | — |
| ProofBench | — | 3% |

## Knowledge

- Llama 3.2 1B: 7.2 (#312)
- MiniMax-M2.7: 37.7 (#152)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Expert | 1007 | 1444 |
| GPQA Diamond | 23.9% | — |
| Vectara Hallucination Rate | — | 12.9% |

## Multilingual

- Llama 3.2 1B: 23.8 (#292)
- MiniMax-M2.7: 50.3 (#123)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 973 | 1382 |
| LMArena Chinese | 959 | 1441 |
| LMArena German | 1014 | 1398 |
| LMArena Russian | 941 | 1383 |
| LMArena French | — | 1421 |
| LMArena Japanese | — | 1262 |
| LMArena Korean | — | 1313 |
| LMArena Spanish | — | 1403 |

## Instruction Following

- Llama 3.2 1B: 52.4 (#290)
- MiniMax-M2.7: 74.1 (#103)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1031 | 1405 |

## Long Context

- Llama 3.2 1B: 31.9 (#274)
- MiniMax-M2.7: 43.3 (#99)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1050 | 1419 |

## Writing & Preference

- Llama 3.2 1B: 21.3 (#310)
- MiniMax-M2.7: 58.9 (#112)

| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1055 | 1405 |
| LMArena Creative Writing | 1033 | 1354 |
| LMArena Multi-Turn | 1030 | 1412 |
| EQ-Bench Creative Writing | 200 | — |

## FAQ

### Is Llama 3.2 1B better than MiniMax-M2.7?

MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 20.1 on the Noometry Index. Llama 3.2 1B costs 7.4× 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 3.2 1B or MiniMax-M2.7?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.

### Is Llama 3.2 1B or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.8 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2.7 does, with 205K tokens against 60K.

### How many benchmarks do Llama 3.2 1B and MiniMax-M2.7 share?

14 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and MiniMax-M2.7 has 30.