# GLM-5.2 vs MiniMax-M2.5

> GLM-5.2 is the stronger model overall, scoring 51.1 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-minimax-m2-5
- Last updated: 2026-10-12
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and MiniMax-M2.5 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 26.9.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 16.8% for MiniMax-M2.5.
- MiniMax-M2.5 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 205K.

## Snapshot

| | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| Provider | Z.ai (Zhipu) | MiniMax |
| Noometry Index | 51.1 | 38.3 |
| Rank | 44 | 188 |
| Context | 1M | 205K |
| Input $/M | $1.40 | $0.30 |
| Output $/M | $4.40 | $1.20 |
| Weights | Open | Open |

## Coding

- GLM-5.2: 51.3 (#41)
- MiniMax-M2.5: 48.1 (#58)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena WebDev | 1603 | 1387 |
| LMArena Coding | 1485 | 1381 |
| ALE-Bench | 1,047 | 618.17 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SWE-bench Verified (bash only) | — | 75.8% |
| SWE-bench Multilingual | — | 68.3% |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |

## Agentic & Tool Use

- GLM-5.2: 32.4 (#63)
- MiniMax-M2.5: 30.4 (#77)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| Vending-Bench 2 | 8,314 | -23.16 |
| Terminal-Bench | — | 42.7% |
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |

## Reasoning

- GLM-5.2: 42.3 (#52)
- MiniMax-M2.5: 17.5 (#292)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| ARC-AGI-2 | 22.8% | 4.9% |
| Kagi LLM Benchmark | 62.6% | 55.2% |
| NYT Connections (extended) | 74.3% | 16.8% |
| ARC-AGI-1 | 77% | 63.7% |
| LMArena Hard Prompts | 1480 | 1372 |
| Epoch Capabilities Index | 151.78 | 146.68 |
| SimpleBench | 58.8% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |

## Math

- GLM-5.2: 55.7 (#43)
- MiniMax-M2.5: 26.9 (#253)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| ProofBench | 35% | 4% |
| LMArena Math | 1482 | 1378 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |

## Knowledge

- GLM-5.2: 57.1 (#40)
- MiniMax-M2.5: 39.2 (#135)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena Expert | 1486 | 1379 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 9.1% |

## Multilingual

- GLM-5.2: 55.8 (#26)
- MiniMax-M2.5: 47.1 (#152)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1459 | 1338 |
| LMArena Chinese | 1519 | 1393 |
| LMArena French | 1479 | 1362 |
| LMArena German | 1468 | 1362 |
| LMArena Japanese | 1451 | 1171 |
| LMArena Korean | 1445 | 1232 |
| LMArena Russian | 1466 | 1358 |
| LMArena Spanish | 1477 | 1354 |

## Instruction Following

- GLM-5.2: 76.9 (#34)
- MiniMax-M2.5: 71.5 (#148)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1353 |

## Long Context

- GLM-5.2: 45.3 (#43)
- MiniMax-M2.5: 37.5 (#216)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1479 | 1366 |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |

## Writing & Preference

- GLM-5.2: 70.4 (#21)
- MiniMax-M2.5: 53.9 (#153)

| Benchmark | GLM-5.2 | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1470 | 1359 |
| LMArena Creative Writing | 1462 | 1331 |
| EQ-Bench Creative Writing | 1757 | 1361 |
| LMArena Multi-Turn | 1469 | 1364 |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than MiniMax-M2.5?

GLM-5.2 is the stronger model overall, scoring 51.1 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 or MiniMax-M2.5?

MiniMax-M2.5 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or MiniMax-M2.5 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 48.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 205K.

### How many benchmarks do GLM-5.2 and MiniMax-M2.5 share?

27 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and MiniMax-M2.5 has 33.