# GPT-4o mini vs MiniMax-M2.7

> MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 25.4 on the Noometry Index. GPT-4o mini costs 2.0× 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/gpt-4o-mini-vs-minimax-m2-7
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. GPT-4o mini scores higher in 1 category and MiniMax-M2.7 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiniMax-M2.7 leads 41.4 to 21.6.
- The biggest single-benchmark swing is WeirdML: 11.8% for GPT-4o mini and 37% for MiniMax-M2.7.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
- MiniMax-M2.7 accepts more context: 205K tokens versus 128K.
- MiniMax-M2.7 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 25.4 | 37.4 |
| Rank | 337 | 188 |
| Context | 128K | 205K |
| Input $/M | $0.15 | $0.30 |
| Output $/M | $0.60 | $1.20 |
| Weights | Proprietary | Open |

## Coding

- GPT-4o mini: 21.6 (#330)
- MiniMax-M2.7: 41.4 (#115)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| WeirdML | 11.8% | 37% |
| LMArena Coding | 1290 | 1454 |
| Aider Polyglot | 3.6% | — |
| LMArena WebDev | — | 1397 |
| SciCode | — | 47% |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| BigCodeBench Complete | 57.4% | — |
| ALE-Bench | — | 599.25 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |

## Agentic & Tool Use

- GPT-4o mini: 27.2 (#95)
- MiniMax-M2.7: 24.9 (#103)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | — | 45.1% |
| BALROG | 17.4% | — |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |

## Reasoning

- GPT-4o mini: 8.7 (#343)
- MiniMax-M2.7: 19.0 (#248)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Hard Prompts | 1267 | 1422 |
| Epoch Capabilities Index | 126.56 | 145.84 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| NYT Connections (extended) | — | 24.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 39.3% |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 54.4% | — |
| LiveBench Data Analysis | 50% | — |
| LMCA | 10.4% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |

## Math

- GPT-4o mini: 9.9 (#307)
- MiniMax-M2.7: 24.9 (#254)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Math | 1267 | 1420 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.9% | — |
| ProofBench | — | 3% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |

## Knowledge

- GPT-4o mini: 17.7 (#279)
- MiniMax-M2.7: 37.1 (#151)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Expert | 1235 | 1443 |
| GPQA Diamond | 37.7% | — |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| Vectara Hallucination Rate | — | 12.9% |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |

## Multimodal

- GPT-4o mini: 25.3 (#117)
- MiniMax-M2.7: —

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |

## Multilingual

- GPT-4o mini: 42.2 (#194)
- MiniMax-M2.7: 50.5 (#120)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 1266 | 1382 |
| LMArena Chinese | 1264 | 1439 |
| LMArena French | 1296 | 1422 |
| LMArena German | 1272 | 1398 |
| LMArena Japanese | 1216 | 1261 |
| LMArena Korean | 1195 | 1313 |
| LMArena Russian | 1275 | 1383 |
| LMArena Spanish | 1276 | 1403 |

## Instruction Following

- GPT-4o mini: 62.1 (#234)
- MiniMax-M2.7: 74.2 (#101)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1259 | 1404 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |

## Long Context

- GPT-4o mini: 39.1 (#181)
- MiniMax-M2.7: 43.3 (#94)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1289 | 1419 |

## Writing & Preference

- GPT-4o mini: 39.7 (#243)
- MiniMax-M2.7: 59.1 (#107)

| Benchmark | GPT-4o mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1286 | 1404 |
| LMArena Creative Writing | 1268 | 1354 |
| LMArena Multi-Turn | 1285 | 1413 |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| LiveBench Language | 28.6% | — |

## FAQ

### Is GPT-4o mini better than MiniMax-M2.7?

MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 25.4 on the Noometry Index. GPT-4o mini costs 2.0× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.

### Which is cheaper, GPT-4o mini or MiniMax-M2.7?

GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.

### Is GPT-4o mini or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 21.6 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2.7 does, with 205K tokens against 128K.

### How many benchmarks do GPT-4o mini and MiniMax-M2.7 share?

19 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and MiniMax-M2.7 has 30.