# GPT-5 vs MiniMax-M2.7

> GPT-5 is the stronger model overall, scoring 50.9 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 6.5× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-vs-minimax-m2-7
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. GPT-5 scores higher in 8 categories and MiniMax-M2.7 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 leads 55.0 to 25.9.
- The biggest single-benchmark swing is WeirdML: 60.7% for GPT-5 and 37% for MiniMax-M2.7.
- MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 205K.
- MiniMax-M2.7 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 50.9 | 37.7 |
| Rank | 45 | 196 |
| Context | 400K | 205K |
| Input $/M | $1.25 | $0.30 |
| Output $/M | $10 | $1.20 |
| Weights | Proprietary | Open |

## Coding

- GPT-5: 50.3 (#47)
- MiniMax-M2.7: 41.8 (#120)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena WebDev | 1418 | 1398 |
| SciCode | 42.9% | 47% |
| WeirdML | 60.7% | 37% |
| LMArena Coding | 1436 | 1454 |
| ALE-Bench | 1,162 | 599.25 |
| SWE-bench Verified | 73.6% | — |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| GSO | 6.9% | — |
| AlgoTune | 1.67 | — |

## Agentic & Tool Use

- GPT-5: 33.1 (#56)
- MiniMax-M2.7: 25.1 (#111)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | 49.6% | 45.1% |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |

## Reasoning

- GPT-5: 38.3 (#64)
- MiniMax-M2.7: 19.7 (#253)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| CritPt | 12.6% | 0.6% |
| LMArena Hard Prompts | 1416 | 1422 |
| Epoch Capabilities Index | 150 | 145.85 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| NYT Connections (extended) | — | 24.7% |
| ARC-AGI-1 | 65.7% | — |
| Chess Puzzles | 37% | — |
| EnigmaEval | 10.5% | — |
| Thematic Generalization | — | 39.3% |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.7% | — |
| LMCA | 40% | — |
| ForecastBench | 61.4 | — |

## Math

- GPT-5: 55.0 (#44)
- MiniMax-M2.7: 25.9 (#263)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| ProofBench | 18% | 3% |
| LMArena Math | 1407 | 1420 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |

## Knowledge

- GPT-5: 56.6 (#43)
- MiniMax-M2.7: 37.7 (#152)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| Vectara Hallucination Rate | 14.7% | 12.9% |
| LMArena Expert | 1419 | 1444 |
| GPQA Diamond | 86.2% | — |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| GPQA (HELM) | 79.2% | — |

## Multimodal

- GPT-5: 46.8 (#13)
- MiniMax-M2.7: —

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |

## Multilingual

- GPT-5: 51.4 (#110)
- MiniMax-M2.7: 50.3 (#123)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 1397 | 1382 |
| LMArena Chinese | 1422 | 1441 |
| LMArena French | 1410 | 1421 |
| LMArena German | 1416 | 1398 |
| LMArena Japanese | 1409 | 1262 |
| LMArena Korean | 1360 | 1313 |
| LMArena Russian | 1406 | 1383 |
| LMArena Spanish | 1399 | 1403 |

## Instruction Following

- GPT-5: 73.8 (#113)
- MiniMax-M2.7: 74.1 (#103)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1388 | 1405 |
| IFEval | 87.5% | — |

## Long Context

- GPT-5: 69.5 (#2)
- MiniMax-M2.7: 43.3 (#99)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1399 | 1419 |
| Fiction.LiveBench | 97.2% | — |

## Writing & Preference

- GPT-5: 63.4 (#65)
- MiniMax-M2.7: 58.9 (#112)

| Benchmark | GPT-5 | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1406 | 1405 |
| LMArena Creative Writing | 1365 | 1354 |
| LMArena Multi-Turn | 1426 | 1412 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |

## FAQ

### Is GPT-5 better than MiniMax-M2.7?

GPT-5 is the stronger model overall, scoring 50.9 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 6.5× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.

### Which is cheaper, GPT-5 or MiniMax-M2.7?

MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GPT-5 lists at $1.25 and $10.

### Is GPT-5 or MiniMax-M2.7 better for coding?

GPT-5 scores higher on coding benchmarks: 50.3 versus 41.8 in the Noometry coding category.

### Which has the bigger context window?

GPT-5 does, with 400K tokens against 205K.

### How many benchmarks do GPT-5 and MiniMax-M2.7 share?

26 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and MiniMax-M2.7 has 30.