# GLM-4.7 vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 41.7 on the Noometry Index. GLM-4.7 costs 2.0× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-muse-spark-1-3
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. GLM-4.7 scores higher in 1 category and Muse Spark 1.3 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 72.4 to 37.7.
- The biggest single-benchmark swing is Chess Puzzles: 6% for GLM-4.7 and 38% for Muse Spark 1.3.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.3.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.7 | 54.4 |
| Rank | 117 | 27 |
| Context | 205K | 1.05M |
| Input $/M | $0.60 | $1.25 |
| Output $/M | $2.20 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.7: 43.6 (#74)
- Muse Spark 1.3: 56.1 (#18)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1435 | 1655 |
| SciCode | 45.1% | 59.7% |
| LMArena Coding | 1454 | 1516 |
| CursorBench | — | 41.6% |
| ALE-Bench | 399.48 | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Muse Spark 1.3: 38.6 (#28)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 2,377 | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Muse Spark 1.3: 52.9 (#27)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| CritPt | 1.7% | 24.9% |
| Chess Puzzles | 6% | 38% |
| LMArena Hard Prompts | 1443 | 1503 |
| Epoch Capabilities Index | 143.53 | 156.89 |
| SimpleBench | 47.7% | — |
| NYT Connections (extended) | — | 85.1% |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |

## Math

- GLM-4.7: 37.7 (#92)
- Muse Spark 1.3: 72.4 (#16)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 99.2% |
| LMArena Math | 1423 | 1497 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Muse Spark 1.3: 42.0 (#92)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1424 | 1516 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |

## Multimodal

- GLM-4.7: —
- Muse Spark 1.3: 43.0 (#21)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1310 |
| LMArena Document | — | 1471 |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Muse Spark 1.3: 57.8 (#6)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1417 | 1485 |
| LMArena Chinese | 1495 | 1527 |
| LMArena French | 1432 | 1534 |
| LMArena Korean | 1401 | 1513 |
| LMArena Russian | 1423 | 1491 |
| LMArena Spanish | 1433 | 1485 |
| LMArena German | 1426 | — |
| LMArena Japanese | 1439 | — |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Muse Spark 1.3: 77.8 (#14)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1411 | 1481 |

## Long Context

- GLM-4.7: 42.8 (#111)
- Muse Spark 1.3: 45.7 (#29)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1432 | 1491 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Muse Spark 1.3: 73.7 (#10)

| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1436 | 1490 |
| LMArena Creative Writing | 1401 | 1450 |
| EQ-Bench Creative Writing | 1413 | 1906 |
| LMArena Multi-Turn | 1446 | 1486 |

## FAQ

### Is GLM-4.7 better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 41.7 on the Noometry Index. GLM-4.7 costs 2.0× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7 or Muse Spark 1.3?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.

### Is GLM-4.7 or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.1 versus 43.6 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.3 does, with 1.05M tokens against 205K.

### How many benchmarks do GLM-4.7 and Muse Spark 1.3 share?

22 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Muse Spark 1.3 has 34.