# MiniMax-M2.1 vs o4-mini

> o4-mini is the stronger model overall, scoring 41.6 to 38.9 on the Noometry Index. MiniMax-M2.1 costs 3.7× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/minimax-m2-1-vs-o4-mini
- Last updated: 2026-10-11
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. MiniMax-M2.1 scores higher in 2 categories and o4-mini in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where o4-mini leads 24.6 to 16.6.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 11.8% for MiniMax-M2.1 and 18.6% for o4-mini.
- MiniMax-M2.1 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- MiniMax-M2.1 accepts more context: 205K tokens versus 200K.
- MiniMax-M2.1 has downloadable open weights; the other is API-only.

## Snapshot

| | MiniMax-M2.1 | o4-mini |
|---|---|---|
| Provider | MiniMax | OpenAI |
| Noometry Index | 38.9 | 41.6 |
| Rank | 178 | 132 |
| Context | 205K | 200K |
| Input $/M | $0.30 | $1.10 |
| Output $/M | $1.20 | $4.40 |
| Weights | Open | Proprietary |

## Coding

- MiniMax-M2.1: 40.4 (#143)
- o4-mini: 40.9 (#127)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Coding | 1421 | 1368 |
| ALE-Bench | 623.83 | 826.17 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| LMArena WebDev | 1384 | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |

## Agentic & Tool Use

- MiniMax-M2.1: 27.9 (#98)
- o4-mini: 32.6 (#61)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| Terminal-Bench | 36.6% | — |
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |

## Reasoning

- MiniMax-M2.1: 16.6 (#302)
- o4-mini: 24.6 (#162)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Hard Prompts | 1411 | 1351 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| NYT Connections (extended) | 11.2% | — |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| Epoch Capabilities Index | — | 145.64 |
| ForecastBench | — | 61.8 |

## Math

- MiniMax-M2.1: 38.3 (#138)
- o4-mini: 40.8 (#89)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Math | 1397 | 1389 |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |

## Knowledge

- MiniMax-M2.1: 38.3 (#147)
- o4-mini: 43.6 (#91)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| Vectara Hallucination Rate | 11.8% | 18.6% |
| LMArena Expert | 1431 | 1343 |
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| GPQA (HELM) | — | 73.5% |

## Multimodal

- MiniMax-M2.1: —
- o4-mini: 40.2 (#49)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |

## Multilingual

- MiniMax-M2.1: 50.0 (#128)
- o4-mini: 47.0 (#154)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Non-English | 1378 | 1337 |
| LMArena Chinese | 1430 | 1354 |
| LMArena French | 1404 | 1364 |
| LMArena German | 1381 | 1336 |
| LMArena Japanese | 1287 | 1308 |
| LMArena Korean | 1298 | 1312 |
| LMArena Russian | 1387 | 1334 |
| LMArena Spanish | 1397 | 1347 |

## Instruction Following

- MiniMax-M2.1: 73.8 (#112)
- o4-mini: 75.2 (#68)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1400 | 1321 |
| IFEval | — | 92.8% |

## Long Context

- MiniMax-M2.1: 43.2 (#101)
- o4-mini: 45.5 (#33)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Longer Query | 1416 | 1315 |
| Fiction.LiveBench | — | 77.8% |

## Writing & Preference

- MiniMax-M2.1: 58.3 (#120)
- o4-mini: 54.0 (#152)

| Benchmark | MiniMax-M2.1 | o4-mini |
|---|---|---|
| LMArena Text | 1392 | 1353 |
| LMArena Creative Writing | 1361 | 1294 |
| LMArena Multi-Turn | 1396 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |

## FAQ

### Is MiniMax-M2.1 better than o4-mini?

o4-mini is the stronger model overall, scoring 41.6 to 38.9 on the Noometry Index. MiniMax-M2.1 costs 3.7× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.

### Which is cheaper, MiniMax-M2.1 or o4-mini?

MiniMax-M2.1 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; o4-mini lists at $1.10 and $4.40.

### Is MiniMax-M2.1 or o4-mini better for coding?

They score almost the same on coding (40.4 vs 40.9); test both on your own repository before choosing.

### Which has the bigger context window?

MiniMax-M2.1 does, with 205K tokens against 200K.

### How many benchmarks do MiniMax-M2.1 and o4-mini share?

19 benchmarks have published results for both models. MiniMax-M2.1 has 22 scored results on Noometry and o4-mini has 60.