# GLM-5.2 vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 50.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-muse-spark-1-3
- Last updated: 2026-10-10
- Shared benchmarks: 28

## Summary

- They share 28 benchmarks with published results for both. GLM-5.2 scores higher in 1 category and Muse Spark 1.3 in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 72.4 to 55.4.
- The biggest single-benchmark swing is FrontierMath Tier 4: 29.3% for GLM-5.2 and 46.3% for Muse Spark 1.3.
- Muse Spark 1.3 is cheaper at $1.25 / $4.25 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 1M.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 50.9 | 54.4 |
| Rank | 38 | 27 |
| Context | 1M | 1.05M |
| Input $/M | $1.40 | $1.25 |
| Output $/M | $4.40 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.2: 50.9 (#37)
- Muse Spark 1.3: 56.1 (#18)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1604 | 1655 |
| SciCode | 50.5% | 59.7% |
| LMArena Coding | 1484 | 1516 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| CursorBench | — | 41.6% |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- Muse Spark 1.3: 38.6 (#28)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- Muse Spark 1.3: 52.9 (#27)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| NYT Connections (extended) | 74.3% | 85.1% |
| CritPt | 20.9% | 24.9% |
| Chess Puzzles | 21% | 38% |
| LMArena Hard Prompts | 1480 | 1503 |
| Mystery Game Puzzles | 19% | 25% |
| DTBench | 93.6% | 96.5% |
| LMCA | 45.8% | 53.9% |
| Epoch Capabilities Index | 151.76 | 156.89 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |
| Bench to the Future 3 | — | 0.14 |

## Math

- GLM-5.2: 55.4 (#35)
- Muse Spark 1.3: 72.4 (#16)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 74.4% |
| FrontierMath Tier 4 | 29.3% | 46.3% |
| OTIS Mock AIME 2024-2025 | 86.4% | 99.2% |
| LMArena Math | 1482 | 1497 |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |

## Knowledge

- GLM-5.2: 57.1 (#36)
- Muse Spark 1.3: 42.0 (#92)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1484 | 1516 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |

## Multimodal

- GLM-5.2: —
- Muse Spark 1.3: 43.0 (#21)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1310 |
| LMArena Document | — | 1471 |

## Multilingual

- GLM-5.2: 56.0 (#25)
- Muse Spark 1.3: 57.8 (#6)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1459 | 1485 |
| LMArena Chinese | 1518 | 1527 |
| LMArena French | 1476 | 1534 |
| LMArena Korean | 1445 | 1513 |
| LMArena Russian | 1467 | 1491 |
| LMArena Spanish | 1477 | 1485 |
| LMArena German | 1470 | — |
| LMArena Japanese | 1451 | — |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- Muse Spark 1.3: 77.8 (#14)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1481 |

## Long Context

- GLM-5.2: 45.3 (#43)
- Muse Spark 1.3: 45.7 (#29)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1479 | 1491 |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- Muse Spark 1.3: 73.7 (#10)

| Benchmark | GLM-5.2 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1470 | 1490 |
| LMArena Creative Writing | 1465 | 1450 |
| EQ-Bench Creative Writing | 1757 | 1906 |
| LMArena Multi-Turn | 1468 | 1486 |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 50.9 on the Noometry Index.

### Which is cheaper, GLM-5.2 or Muse Spark 1.3?

Muse Spark 1.3 is cheaper. It lists at $1.25 per million input tokens and $4.25 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.1 versus 50.9 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.3 does, with 1.05M tokens against 1M.

### How many benchmarks do GLM-5.2 and Muse Spark 1.3 share?

28 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Muse Spark 1.3 has 34.