# MiniMax-M2.5 vs o4-mini

> o4-mini is the stronger model overall, scoring 41.6 to 38.1 on the Noometry Index. MiniMax-M2.5 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-m2-5-vs-o4-mini
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. MiniMax-M2.5 scores higher in 2 categories and o4-mini in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where o4-mini leads 40.2 to 25.8.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 75.8% for MiniMax-M2.5 and 45% for o4-mini.
- MiniMax-M2.5 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- MiniMax-M2.5 accepts more context: 205K tokens versus 200K.
- MiniMax-M2.5 has downloadable open weights; the other is API-only.

## Snapshot

| | MiniMax-M2.5 | o4-mini |
|---|---|---|
| Provider | MiniMax | OpenAI |
| Noometry Index | 38.1 | 41.6 |
| Rank | 181 | 119 |
| Context | 205K | 200K |
| Input $/M | $0.30 | $1.10 |
| Output $/M | $1.20 | $4.40 |
| Weights | Open | Proprietary |

## Coding

- MiniMax-M2.5: 47.9 (#53)
- o4-mini: 41.6 (#110)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| SWE-bench Verified (bash only) | 75.8% | 45% |
| LMArena Coding | 1380 | 1369 |
| ALE-Bench | 618.17 | 826.17 |
| Aider Polyglot | — | 72% |
| LMArena WebDev | 1386 | — |
| SWE-bench Multilingual | 68.3% | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |

## Agentic & Tool Use

- MiniMax-M2.5: 30.2 (#70)
- o4-mini: 32.3 (#57)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| Terminal-Bench | 42.7% | — |
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
| Vending-Bench 2 | -23.16 | — |

## Reasoning

- MiniMax-M2.5: 17.6 (#281)
- o4-mini: 24.6 (#143)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| ARC-AGI-2 | 4.9% | 6.1% |
| Kagi LLM Benchmark | 55.2% | 67.6% |
| ARC-AGI-1 | 63.7% | 58.7% |
| LMArena Hard Prompts | 1372 | 1351 |
| Epoch Capabilities Index | 146.68 | 145.63 |
| SimpleBench | — | 38.7% |
| NYT Connections (extended) | 16.8% | — |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| ForecastBench | — | 61.8 |

## Math

- MiniMax-M2.5: 25.8 (#242)
- o4-mini: 40.2 (#79)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Math | 1378 | 1390 |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| ProofBench | 4% | — |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |

## Knowledge

- MiniMax-M2.5: 38.6 (#132)
- o4-mini: 43.5 (#83)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| Vectara Hallucination Rate | 9.1% | 18.6% |
| LMArena Expert | 1378 | 1344 |
| 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-M2.5: —
- o4-mini: 39.5 (#46)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |

## Multilingual

- MiniMax-M2.5: 47.3 (#147)
- o4-mini: 47.2 (#149)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Non-English | 1338 | 1336 |
| LMArena Chinese | 1392 | 1355 |
| LMArena French | 1363 | 1362 |
| LMArena German | 1361 | 1336 |
| LMArena Japanese | 1169 | 1308 |
| LMArena Korean | 1232 | 1313 |
| LMArena Russian | 1359 | 1334 |
| LMArena Spanish | 1354 | 1346 |

## Instruction Following

- MiniMax-M2.5: 71.7 (#143)
- o4-mini: 75.4 (#63)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1353 | 1321 |
| IFEval | — | 92.8% |

## Long Context

- MiniMax-M2.5: 37.4 (#211)
- o4-mini: 45.5 (#31)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Longer Query | 1367 | 1315 |
| Fiction.LiveBench | — | 77.8% |
| CL-bench | 11.4% | — |
| CL-bench Life | 6.3% | — |

## Writing & Preference

- MiniMax-M2.5: 54.1 (#149)
- o4-mini: 54.2 (#148)

| Benchmark | MiniMax-M2.5 | o4-mini |
|---|---|---|
| LMArena Text | 1359 | 1353 |
| LMArena Creative Writing | 1331 | 1294 |
| LMArena Multi-Turn | 1364 | 1350 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 1361 | — |
| WildBench | — | 85.4% |

## FAQ

### Is MiniMax-M2.5 better than o4-mini?

o4-mini is the stronger model overall, scoring 41.6 to 38.1 on the Noometry Index. MiniMax-M2.5 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-M2.5 or o4-mini?

MiniMax-M2.5 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-M2.5 or o4-mini better for coding?

MiniMax-M2.5 scores higher on coding benchmarks: 47.9 versus 41.6 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2.5 does, with 205K tokens against 200K.

### How many benchmarks do MiniMax-M2.5 and o4-mini share?

24 benchmarks have published results for both models. MiniMax-M2.5 has 33 scored results on Noometry and o4-mini has 60.