# Llama 3.2 1B vs MiniMax-M3

> MiniMax-M3 is the stronger model overall, scoring 43.8 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-M3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-2-1b-vs-minimax-m3
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and MiniMax-M3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax-M3 leads 58.4 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 0.6% for Llama 3.2 1B and 71.1% for MiniMax-M3.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M3.
- MiniMax-M3 accepts more context: 1M tokens versus 60K.

## Snapshot

| | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 20.1 | 43.8 |
| Rank | 354 | 85 |
| Context | 60K | 1M |
| Input $/M | $0.027 | $0.30 |
| Output $/M | $0.20 | $1.20 |
| Weights | Open | Open |

## Coding

- Llama 3.2 1B: 21.1 (#338)
- MiniMax-M3: 41.8 (#118)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Coding | 1070 | 1469 |
| FrontierCode | — | 14.7% |
| LMArena WebDev | — | 1482 |
| SciCode | — | 47.1% |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| ALE-Bench | — | 640.02 |

## Agentic & Tool Use

- Llama 3.2 1B: 14.6 (#150)
- MiniMax-M3: 22.6 (#130)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| APEX-Agents | — | 37.7% |
| Berkeley Function Calling Leaderboard | 10.8% | — |
| OSWorld 2.0 | — | 4.6% |
| BALROG | 6.6% | — |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 2,158 |

## Reasoning

- Llama 3.2 1B: 16.2 (#308)
- MiniMax-M3: 30.1 (#87)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| Chess Puzzles | 0% | 14% |
| LMArena Hard Prompts | 1044 | 1447 |
| Epoch Capabilities Index | 101.99 | 146.95 |
| SimpleBench | — | 45.8% |
| NYT Connections (extended) | — | 65.1% |
| CritPt | — | 3.7% |
| Mystery Game Puzzles | — | 8% |
| DTBench | — | 78.9% |
| LMCA | — | 33.7% |
| Surface Evolver Bench | — | 55% |
| ForecastBench | — | 61.4 |

## Math

- Llama 3.2 1B: 10.4 (#313)
- MiniMax-M3: 40.0 (#95)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 71.1% |
| LMArena Math | 1086 | 1429 |
| ProofBench | — | 18% |

## Knowledge

- Llama 3.2 1B: 7.2 (#312)
- MiniMax-M3: 58.4 (#35)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| GPQA Diamond | 23.9% | 90.9% |
| LMArena Expert | 1007 | 1461 |

## Multimodal

- Llama 3.2 1B: —
- MiniMax-M3: 40.2 (#51)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Vision | — | 1253 |
| LMArena Document | — | 1435 |

## Multilingual

- Llama 3.2 1B: 23.8 (#292)
- MiniMax-M3: 53.0 (#75)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Non-English | 973 | 1420 |
| LMArena Chinese | 959 | 1463 |
| LMArena German | 1014 | 1426 |
| LMArena Russian | 941 | 1428 |
| LMArena French | — | 1447 |
| LMArena Japanese | — | 1381 |
| LMArena Korean | — | 1372 |
| LMArena Spanish | — | 1432 |

## Instruction Following

- Llama 3.2 1B: 52.4 (#290)
- MiniMax-M3: 75.5 (#62)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Instruction Following | 1031 | 1433 |

## Long Context

- Llama 3.2 1B: 31.9 (#274)
- MiniMax-M3: 44.2 (#72)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Longer Query | 1050 | 1445 |

## Writing & Preference

- Llama 3.2 1B: 21.3 (#310)
- MiniMax-M3: 62.1 (#83)

| Benchmark | Llama 3.2 1B | MiniMax-M3 |
|---|---|---|
| LMArena Text | 1055 | 1433 |
| LMArena Creative Writing | 1033 | 1404 |
| LMArena Multi-Turn | 1030 | 1442 |
| EQ-Bench Creative Writing | 200 | — |
| EQ-Bench 4 | — | 1150 |

## FAQ

### Is Llama 3.2 1B better than MiniMax-M3?

MiniMax-M3 is the stronger model overall, scoring 43.8 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-M3's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.2 1B or MiniMax-M3?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; MiniMax-M3 lists at $0.30 and $1.20.

### Is Llama 3.2 1B or MiniMax-M3 better for coding?

MiniMax-M3 scores higher on coding benchmarks: 41.8 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M3 does, with 1M tokens against 60K.

### How many benchmarks do Llama 3.2 1B and MiniMax-M3 share?

17 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and MiniMax-M3 has 41.